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@inproceedings{bib_Rank_2025, AUTHOR = {Harshvardhan, and Kumar, Adapa Sunil and Shaik, Rehna and Mishra, Anurag and Prakash, K. Sachin }, TITLE = {Ranking CMIP6 GCMs for Extreme Precipitation Simulation Using Integrated Bias Correction and MultiCriteria Decision Analysis}, BOOKTITLE = {ROORKEE WATER CONCLAVE}. YEAR = {2025}}

This study evaluated the performance of 36 Coupled Model Inter-comparison Project Phase 6 (CMIP6) Global Climate Models (GCMs) in simulating extreme precipitation conditions across India using an integrated bias correction and multi-criteria decision-making framework. Daily precipitation data spanning 1975-2014 from CMIP6 GCMs were bias-corrected against Indian Meteorological Department (IMD) gridded observations at 0.25° spatial resolution using three distinct methods: (i) monthly quantile mapping applying empirical cumulative distribution function transformation on monthly aggregated data, (ii) daily quantile mapping using complete daily time series with linear interpolation for intermediate quantiles, and (iii) ISIMIP3b parametric trend-preserving quantile mapping employing gamma distribution fitting with mixed trend preservation strategy. Three extreme precipitation indices were calculated from bias-corrected data: total annual precipitation on wet days (PRCPTOTAL), 99th percentile of daily precipitation (R99p), frequency of days exceeding 99th percentile (R99pf). Performance evaluation employed Taylor Skill Score incorporating correlation and variance ratios, Pearson correlation coefficient (PCC), root-mean-square error (RMSE), and normalised standard deviation against IMD reference data. For the R99p index, PCC values ranged from -0.045 to 0.628, RMSE varied from 29.86 to 58.23 mm, and Taylor Skill Scores ranged from 0.72 to 0.17 across all GCMs. For R99p frequency, PCC ranged from 0.242 to 0.663, RMSE varied from 0.526 to 1.380, and Taylor Skill Scores ranged from 0.83 to 0.59. Multi-criteria decision-making integration used Analytic Hierarchy Process (AHP) with expert judgment-based pairwise comparison matrices for criteria weighting, followed by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VlseKriterijumska Optimizacija Kompromisno Resenje (VIKOR) ranking methods with entropy-based objective weighting. The comprehensive ranking revealed EC-Earth3-Veg as the best performing GCM with a combined TOPSIS score of 0.890, followed by EC-Earth3-CC (0.880) and EC-Earth3 (0.875). MIROC6 achieved the highest Taylor Skill Score (0.717) for R99p intensity simulation, while GFDL-CM4 demonstrated consistent performance across both intensity and frequency metrics.

@inproceedings{bib_COMP_2025, AUTHOR = {Harshvardhan, and Kumar, Adapa Sunil and Shaik, Rehna and Mishra, Anurag and Prakash, K. Sachin }, TITLE = {COMPREHENSIVE MULTI-CRITERIA RANKING OF CMIP6 MODELS FOR HOT AND COLD EXTREME INDICES OVER INDIAN SUBCONTINENT}, BOOKTITLE = {International Conference on Hydraulics, Water Resources and Coastal Engineering}. YEAR = {2025}}

This study presents a comprehensive multi-criteria ranking framework for evaluating 36 CMIP6 global climate models based on their simulation of temperature extreme indices over the Indian subcontinent. The research focuses on comparative assessment of Ta95p (95th percentile, hot extremes) and Ta5p (5th percentile, cold extremes) across critical monsoon (JJAS) and post-monsoon (OND) seasons. The methodology employs a systematic data processing pipeline beginning with IMD observational data (0.25°×0.25° resolution, 1975-2014) as reference, followed by CMIP6 model data preprocessing including spatial cropping to Indian domain (6.5°N-38.5°N, 66.5°E-100.0°E), bilinear interpolation to IMD resolution, and quantile mapping bias correction. Seasonal temperature extreme indices are calculated using robust percentile estimation methods for both seasons. The ranking framework integrates Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) multi-criteria decision-making techniques with AHP-derived weights (Pearson Correlation Coefficient, PCC: 0.652, Root Mean Square Error, RMSE: 0.239, Standard Deviation Normalization StdNorm: 0.109). TOPSIS calculates distances to ideal positive and negative solutions in multi-dimensional performance space, while VIKOR employs compromise programming emphasizing balanced performance across criteria. Final rankings aggregate both methods using mean rank averaging. Comparative analysis reveals distinct seasonal and index-specific performance patterns: TaiESM1 and CMCC-CM2-SR5 dominate hot extreme (Ta95p) simulation in both seasons, while EC-Earth3-CC and GFDL-ESM4 excel in monsoon cold extremes (Ta5p), and IPSL-CM5A2-INCA leads post-monsoon cold extreme representation. Performance metrics demonstrate substantial inter-model variability with PCC ranging 0.791-0.994, RMSE spanning 0.290-1.989°C, and StdNorm varying 0.835-1.213 across different indices and seasons.

@inproceedings{bib_RAIN_2025, AUTHOR = {Reddy, Gopireddy Surendra Kumar and Harshvardhan, and Shaik, Rehna and Mishra, Anurag and Prakash, K. Sachin }, TITLE = {RAINFALL PREDICTION IN INDIA: A COMPREHENSIVE ANALYSIS OF STATISTICAL DOWNSCALING WITH CMIP6 DATA AND MACHINE LEARNING}, BOOKTITLE = {International Conference on Hydraulics, Water Resources and Coastal Engineering}. YEAR = {2025}}

This study presents a comprehensive analysis of a statistical downscaling framework developed to predict daily rainfall over India. Utilizing outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the National Centers for Environmental Prediction (NCEP) reanalysis data, the study employs a hybrid machine learning approach combining a classification model for broad meteorological zones with a cell-by-cell regression pipeline. The performance of six regression models- Lasso, Ridge, MLP Regressor, Random Forest, XGBoost, and Support Vector Regressor (SVR)—were evaluated using standard metrics. The findings reveal that while the framework successfully generates high-resolution rainfall projections, the models demonstrate limited explanatory power, with R² values ranging from 0.03 to 0.154. A critical discrepancy between the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) indicates a significant failure to accurately predict extreme rainfall events, a key limitation for practical applications. The analysis concludes that while the framework provides a valuable baseline, a move towards more sophisticated architectures, such as deep learning and hybrid statistical-dynamical methods, is essential to capture the complex, non-linear spatiotemporal dynamics of precipitation.

@inproceedings{bib_Spat_2025, AUTHOR = {Latwal, Avantika and Shaik, Rehna and Rajan, Krishnan Sundara }, TITLE = {Spatiotemporal analysis of Turbidity in Tropical Reservoirs using Sentinel-2 Satellite Imagery under Catchment Dynamics}, BOOKTITLE = {Remote Sensing Applications: Society and Environment}. YEAR = {2025}}

Contributing catchment land use patterns under rapid urbanization, agricultural practices and climatic variability in tropical regions significantly influence the sediment load of inland waterbodies such as reservoirs. This study presents a modeling framework to estimate qualitative turbidity dynamics in two tropical reservoirs (Bhadra and Tungabhadra) within same river basin but different landscape characteristics, using Sentinel-2 satellite data from 2016 to 2021 using Google Earth Engine (GEE). The framework demonstrates a robust capability of Sentinel-2 data, in detecting, mapping spatiotemporal trends and classifying turbidity levels from low to high along with impact of potential catchment characteristics. The Normalized Difference Turbidity Index (NDTI) values have a strong correlation with in-situ data (Earth Observing and Mapping EOMAP and field-based observations) having values > 10 NTU (nephelometric turbidity unit) with R2 = 0.81 and standard Error Estimate (SEE) of 5.3 respectively. In addition, the correlation between red band reflectance values with <10 NTU, has demonstrated a strong relation with an R2 and SEE of 0.74 and 1.7 respectively. Thus, the combination of NDTI and single red band reflectance values were used further for classifying the different turbidity levels (low, moderate and high). It was also observed that diverse land use patterns, particularly high proportions of urban and agriculture area along with rainfall, impact the annual and seasonal fluctuation of turbidity level in the reservoirs. The study demonstrates that remote sensing (RS) approach can effectively estimate turbidity levels in tropical reservoirs, highlighting the impact of catchment characteristics and supporting improved monitoring and sustainable water resource management.

@inproceedings{bib_Iot__2025, AUTHOR = {Sutradhar, Amit and Kumar, Mummidivarapu Satish and Chaudhari, Sachin and Shaik, Rehna }, TITLE = {Iot Based Self-cleaning Water Quality Monitoring System for Sewage Treatment Plant}, BOOKTITLE = {International Conference, Asia Oceania Geosciences Society}. YEAR = {2025}}

Sewage Treatment Plants (STPs) are critical for public health and environmental sustainability. However, Traditional water quality monitoring in globally heavily relies on labour-intensive, lab-based methods, which pose significant challenges but in recent years, IoT-based sensors have emerged as a promising alternative. However, these sensors face challenges including sensor fouling and frequent manual maintenance, which compromise long-term accuracy and operational efficiency. This study presents an innovative IoT enabled Self-Cleaning Water Quality Monitoring System (SC-WQMS) to address these challenges through automation and precision. The proposed system integrates advanced features, including self-cleaning mechanisms. Key water quality parameter, such as Total Dissolved Solids (TDS) is analysed in real-time by using IoT-based sensors. The proposed IoT-enabled SC-WQMS demonstrates significantly better accuracy over traditional non-cleaning devices. Lab report data over a two-month period reveals that the SC-WQMS maintains an average accuracy of 95%-98.5% for TDS measurements, compared to 68%-75% accuracy observed in traditional IoT-based non cleaning devices due to issues like algae formation and particle buildup over the same period. This substantial difference highlights the SC-WQMS's effectiveness in ensuring reliable data transmission to a cloud server. By mitigating the challenges faced by traditional methods. The proposed SC-WQMS represents a scalable, efficient, and sustainable solution for modern water treatment facilities, addressing the operational challenges of traditional systems while promoting better resource management and environmental protection.

@inproceedings{bib_Appl_2024, AUTHOR = {S, Mohammad Shikaf Ali and Shaik, Rehna and Ramana, R. Venkata and Rao, Y. R. Satyaji and Kumar, Mummidivarapu Satish and Rahman, Ataur }, TITLE = {Application of SWMM for Urban Storm Water Management: A Case Study of Hyderabad City}, BOOKTITLE = {nternational Conference on Advancements in Engineering Education}. YEAR = {2024}}

Urban stormwater management involves strategies to mitigate the adverse effects of urban runoff. These strategies aim to reduce flood risk and enhance the environmental values and sustainability of urban water systems. But the effective urban stormwater management is highly dependent on the factors such as spatial variability of urban watershed characteristics as well as the scale of the area under consideration. This insight has prompted increasing utilization of physically based urban watershed models such as the Environmental Protection Agency (EPA) Stormwater Management Model (SWMM). Thus, in this study we utilized SWMM for modeling the dynamic runoff response in a stormwater zone of Hyderabad, India. The study results are validated for a particular observed 48-hour storm event occurred on 3rd and 4th of October 2015. Five years (2010-2014) of hourly rainfall data have been used to estimate the design storms with 2, 5, 10, and 25 years return periods using Gumbel distribution. The results indicate that the existing drainage system is insufficient to withstand the generated runoff by the design storm of 2-year return period and causes notable flooding. The water elevation profile generated from the initial node, representing the beginning of the drainage system, to the outfall indicates that the flooding occurred at the outfall during the peak precipitation event. Also, the hydrographs which were developed through SWMM at different links and outfalls are crucial in designing the sumps or ponds and determining pump capacities, aiding in effective stormwater management practices.

@inproceedings{bib_Drou_2024, AUTHOR = {Kumari, Pallavi and Shaik, Rehna and SHARATHCHANDRA, VANNAM and Singh, Shailesh Kumar }, TITLE = {Drought evaluation using various evapotranspiration models over semi-arid river basins}, BOOKTITLE = {JOURNAL OF WATER AND CLIMATE CHANGE}. YEAR = {2024}}

Multivariate drought indices including various hydrological processes into account can be more valuable under climatic and anthropogenic changes. Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized precipitation Actual Evapotranspiration Index (SPAEI) are the drought indices used to estimate drought index, considering precipitation, and evapotranspiration (ET) into account. Many studies used empirical, machine learning, and process-based model estimates of ET for the calculation of drought indices of SPEI and SPAEI. However, the sensitivity of ET estimates on drought characteristics at the catchment scale is highly complex. The present study aimed to include and analyze the sensitivity of various approaches of empirical (Budyko, Penman–Monteith, Hargreaves, and Turc), modeled (SWAT), and remote sensing (MODIS) in the drought characterization using SPEI and SPAEI. The present methodology was tested on a dry-sub-humid river catchment of India, the Tunga-Bhadra River catchment for the period of 2000–2012. The performance of statistical indicators (Nash–Sutcliffe Efficiency and R2) for SPEI values by various empirical methods of Potential Evapotranspiration (PET) (i) Penman–Monteith (ii) Hargreaves against remote sensing PET were 0.93 and 0.95, respectively, which are high in comparison with SWAT simulated PET-based SPEI values, which shows NSE values of 0.85 against remote sensing PET-based SPEI.

@inproceedings{bib_A_Re_2024, AUTHOR = {Ali, Sabrina and Rahman, Ataur and Shaik, Rehna }, TITLE = {A Review of Event-Based Conceptual Rainfall-Runoff Models: A Case for Australia}, BOOKTITLE = {Encyclopedia}. YEAR = {2024}}

Event-based models focus on modelling of peak runoff from rainfall data. Conceptual models indicate simplified models that provide reasonably accurate answers despite their crude nature. Rainfall-runoff models are used to transform a rainfall event into a runoff event. This paper focuses on reviewing computational simulation of rainfall-runoff processes over a catchment. Lumped conceptual, event-based rainfall-runoff models have remained the dominant practice for design flood estimation in Australia for many years due to their simplicity, flexibility, and accuracy under certain conditions. Attempts to establish regionalization methods for prediction of design flood hydrographs in ungauged catchments have seen little success. Therefore, as well as reviewing key rainfall-runoff model components for design flood estimation with a special focus on event-based conceptual models, this paper covers the aspects of regionalization to promote their applications to ungauged catchments.

@inproceedings{bib_A_ne_2024, AUTHOR = {Latif, Shahid and Ouarda, Taha B M J and St-Hilaire, André and Souaissi, Zina and Shaik, Rehna }, TITLE = {A new nonparametric copula framework for the joint analysis of river water temperature and low flow characteristics for aquatic habitat risk assessment}, BOOKTITLE = {Journal of Hydrology}. YEAR = {2024}}

This study proposed a nonparametric copula hazard framework in the joint risk of river water temperature (RWT) and low flow (LF) events for aquatic ecosystems, specifically ectotherm fish. This nonparametric copula density can adapt to any mutual dependence structure, providing greater flexibility. This can reduce the risk of misspecification if the underlying assumption is violated compared to conventional parametric or semiparametric copula settings. The analysis uses nonparametric copulas densities like Beta kernel copula estimator (BKCE), Bernstein copula estimator (BCE), and Transformation kernel estimator (TKE), conjoined with Gaussian Kernel density estimations (GKDEs) and parametric marginals for joint annual maximum RWT (AMRWT) and LF. The study compares different models for analyzing five Swiss river basins: parametric copulas with best-fitted GKDE and parametric margins, versus nonparametric copula. Six bandwidth selectors are estimated to fit GKDE. BKCE with GKDE margins outperformed most stations, while TKE and BCE density with GKDE margins work best for only one station. However, at one station, BKCE with best-fitted parametric margins is outperformed. All stations except Station 2473 are characterized by temperature and low flows that may be conducive to stress of a number of aquatic species, causing high AMRWT (exceeding 19 °C) and minimum LF or specific discharge, SD) quantiles at low AND-joint return periods (RP). AND (i.e. low flow AND high temperature) hazard events are less likely to occur together than OR hazard events, while univariate RPs happen more often than OR-joint RPs. In addition, the joint RP of AMRWT, given LF at different percentiles, significantly affected AMRWT for various LF conditions. Higher AMRWT with low flow conditions results in lower joint RP (except station 2473), and this stress level would be reduced when conditioned with high LF events at the same AMRWT. Station 2473 has high LF even at low percentiles, and with low AMRWT makes it very less stressful than other station under different LF conditions. Summer river flow maintenance can improve aquatic environments with high RWT. Analyzing joint statistics is crucial to understanding mutual risk in freshwater ecosystems.

@inproceedings{bib_Bibl_2024, AUTHOR = {Shaik, Rehna }, TITLE = {Bibliometric Insights into Microplastic Pollution in Freshwater Ecosystems}, BOOKTITLE = {Water}. YEAR = {2024}}

Microplastic pollution in freshwater ecosystems has emerged as a significant environmental concern, warranting comprehensive investigation, and understanding. This study employs bibliometric analysis to systematically review and synthesize the existing literature on microplastic pollution in freshwater environments from 2013 to 2023. The exponential growth in research output was uncovered by analyzing 885 documents sourced from the Web of Science database, with an average annual growth rate of 73.13% and an average document citation of 30.17. Our findings highlight the dominance of primary and secondary microplastics as pollutants, their ecological consequences, and the resultant socio-economic implications. Notably, the Science of the Total Environment and Environmental Pollution journals emerge as leading publication venues, while China, Germany, and the USA lead in research contributions, underlining the global nature of microplastic pollution research. The analysis further outlines the most commonly cited works, identifying pivotal studies that have shaped current understanding and future research directions. This bibliometric analysis provides a comprehensive overview of the research landscape on microplastic pollution in freshwater ecosystems, helping researchers to identify knowledge gaps and emerging trends. These insights can guide future research directions and inform policymakers and stakeholders on where scientific efforts should be concentrated to better understand and address the impacts of microplastic pollution.

@inproceedings{bib_Vari_2024, AUTHOR = {Shaik, Rehana and Vivek, Nannaka Venkata Sathya and Kumar, Mummidivarapu Satish }, TITLE = {Variations of Compound Warm, Dry, Wet and Cold Climate Extremes in India During 1951–2014}, BOOKTITLE = {Science of the Total Environment}. YEAR = {2024}}

The simultaneous occurrence of climate extremes significantly impacts ecosystems, increasing the vulnerability to physical risks. Despite extensive research on hot extremes and droughts globally, there remains a significant gap in comprehending the occurrence, magnitude, spatial extent, and associated risks of compound extremes, encompassing scenarios like warm/dry, cold/dry, warm/wet, and cold/wet. This study investigates various compound extreme scenarios by examining combinations of maximum temperature (Tx) and the Standardized Precipitation Evapotranspiration Index (SPEI) using monthly data from 1951 to 2014 acquired from India Meteorological Department (IMD) for Indian landmass. From the results, the spatial extent of warm/dry events have increased at 1.8% per decade, while cold/wet events decreased by -1.1% over India. The warm/wet events have shown an increased trend of about 0.3%, and cold/dry events at modest rise of 0.7% per decade. Furthermore, compound warm/dry and cold/wet extremes over India exhibit extreme frequency and shorter return periods, posing greater risk. Conversely, compound cold/dry and warm/wet extremes occur less often, indicating longer return periods and lower risk. Across much of the country, the frequency of warm/dry, cold/dry, warm/wet, and cold/wet extremes ranges from 30-45, 15-30, 20-30, and 25-45 months, respectively. Notably, warm/dry conditions exhibit increased frequency in the recent period (1983-2014) with 31 years compared to the base period (1951-1982) which had approximately 24 years for a spatial extent exceeding 5%. The findings of this study contribute to an enhanced understanding of the changes in compound climate extremes from a multivariate perspective.

@inproceedings{bib_Exam_2024, AUTHOR = {Latwal, Avantika and Kondraju, Tarun and Shaik, Rehana and Rajan, Krishnan Sundara }, TITLE = {Examining chlorophyll-a concentrations in tropical reservoirs under various land use changes using Sentinel – 2 and Google Earth engine – Bhadra and Tungabhadra, India}, BOOKTITLE = {Journal of Contaminant Hydrology}. YEAR = {2024}}

The understanding of spatio-temporal variation in land use and land cover (LULC) patterns is crucial for managing catchment land use planning, as it directly influences of tropical reservoir water quality and the subsequent Nutrient Contamination (NC) of unmonitored water bodies. The current research attempts to accurately measure the influence of LULC and its associated determinants on the quantities of NC loads by using Chl-a as a proxy, within tropical reservoirs, i.e. Bhadra and Tungabhadra, located in same river catchment. This Chl-a spread calculated by the Maximum Chlorophyll Index (MCI) derived from Sentinel 2 satellite data products covering the period from July 2016 to June 2021 were done using Google Earth Engine (GEE) platform. The validation analysis confirms the robustness of the methodology with a strong correlation between MCI-calculated values and EOMAP (Earth Observation and Environmental Services Mapping) Chl-a (μg/L) data points for both reservoirs, Bhadra (R2 = 0.64) and Tungabhadra (R2 = 0.68). The findings reveal that, Tungabhadra reservoir consistently exhibits an excessive spatial distribution of Chl-a spread area (17 km2 to 335 km2 ), reflecting nutrient-rich water inflows, particularly evident during the post-monsoon period. This notable rise could be linked to harvesting the Kharif crop, resulting in elevated nutrient concentrations. In contrast Bhadra reservoir, dominated by forested areas, maintains relatively lower Chl-a spread areas (<20 km2 ), highlighting its pivotal role in maintaining water cleanliness and serves as a riparian boundary. In addition, the changes in LULC classes show a strong relationship with variation in Chl-a during the studied period, for the Bhadra Reservoir R2 = 0.51 (F- statistics = 3.983, p = 0.021), and the Tungabhadra Reservoir R2 = 0.802 (F- statistics = 7.489, p = 0.0143). This highlights how changes in land use significantly shape contamination dynamics, deepening our understanding of nutrient inputs and contamination drivers in tropical reservoirs.

@inproceedings{bib_Asse_2023, AUTHOR = {Shaik, Rehna and Kumar, Mummidivarapu Satish and Reddy, B Jaya Bharat and Ouarda, Taha B M J }, TITLE = {Assessment of Flow Regimes and Water Temperatures: A case study on Yamuna and Bhadra River Systems}, BOOKTITLE = {International Conference on Hydraulics, Water Resources and Coastal Engineering}. YEAR = {2023}}

Risk assessment is vital for comprehending and managing river water quality control. By ensuring a systematic approach for evaluating potential risks, it encourages the adoption of efficient management strategies, the use of informed decision-making, and maintaining of river ecosystems. An effective framework is developed by integrating the empirical based approach with Gumbel-Copula function to estimate the river water quality risk under high water temperatures and low flow rates. Based on Akaike Information Criterion (AIC) among Copula based Gumbel, Gaussian, t, and Clay-ton functions, the Gumbel-Copula function was utilized in the study. The percentiles ranging from 5 to 95 with 5 percentile interval of Stream Flows (SF) and reverse order percentiles of Water Temperatures (WT) are employed in the estimation of risk of river water quality. The current risk quantification approach was implemented using observed datasets on Yamuna (2000 to 2015) and Bhadra (2006 to 2017) river systems. The results reveal that at 50 percentile (SF=380 m3/s and WT=27.38 ℃) the join risk is about 75.29% for Yamuna River and similarly for Bhadra river the risk is about 77.62% (SF=7.20 m3/s and WT=25 ℃).

@inproceedings{bib_Wate_2023, AUTHOR = {Latwal, Avantika and Rajan, Krishnan Sundara and Shaik, Rehana }, TITLE = {Water Quality Estimation Using Remote Sensing Technique: A Case Study of Bhadra Reservoir, Karnataka}, BOOKTITLE = {International Conference on Hydraulics, Water Resources and Coastal Engineering}. YEAR = {2023}}

In the twenty-first century, water quality monitoring is a growing challenge in inland water bodies. Therefore, monitoring the current state of the water bodies is highly important. The remote sensing data is important for effective monitoring that covers a large area. In recent decades, chlorophyll-a (as a proxy) has been a significant indicator of nutrient contamination and also indicates the qualitative status of the water bodies. Besides, the waterbody's temperature also plays an important role in the biological and chemical processes. Hence, the present study aims to detect the chlorophyll-a spread area and temperature of the Bhadra Reservoir, Tungabhadra River system, India. To explore the quality of the reservoir, Sentinel 2A and Landsat 8 satellite images were used for the years 2017 and 2018. The study results noticed that chlorophyll-a spread percent was higher in summer months compared with the water spread area of the reservoir, i.e., 74.5% in 2017 and 61.4% in 2018 from the total water spread area. The study also revealed that as the temperature rises, the chlorophyll-a spread also increases in the reservoir. It was also observed that the October month has a low chlorophyll-a spread, i.e., 32.04% in 2017 and 24.35% in 2018, because the one side reservoir is covered by forest area. Thus, there is a need to explore further to understand the impact of forest area on the reservoir and the temporal changes of the water quality parameters that decide the water bodies quality.

@inproceedings{bib_Spat_2023, AUTHOR = {Shaik, Rehana and Vivek, Nannaka Venkata Sathya }, TITLE = {Spatiotemporal Analysis of Warm and Dry Compound Extreme Events Over India from 1951 to 2014}, BOOKTITLE = {International Conference on Hydraulics, Water Resources and Coastal Engineering}. YEAR = {2023}}

Warm and dry climate extremes have large impact on water resources, agriculture, ecosystems, and human health. The present study used high-resolution rainfall and temperature data at 0.25-degree resolution from 1951 to 2014 to investigate the occurrence of compound warm and dry extreme events across India. The simultaneous occurrence of temperature exceeding the 75th percentile and a Standardized Precipitation Evapotranspiration Index (SPEI) of less than –1 is considered to represent the compound extreme event of warm and dry. The study reveals a strong negative correlation between temperature and SPEI across most of the country except south-west of India. Analysis reveals that about 3.1% of increase in the spatial areal extent of Warm/Dry compound extreme events allover India. The insights derived from this research could significantly contribute to shaping climate change mitigation and adaptation strategies related to compound extreme events.

@inproceedings{bib_Dete_2023, AUTHOR = {Latwal, Avantika and Shaik, Rehana and Rajan, Krishnan Sundara }, TITLE = {Detection and mapping of water and chlorophyll-a spread using Sentinel-2 satellite imagery for water quality assessment of inland water bodies}, BOOKTITLE = {Environmental Monitoring and Assessment}. YEAR = {2023}}

Water quality monitoring of reservoirs is currently a signifcant challenge in the tropical regions of the world due to limited monitoring stations and hydrological data. Remote sensing techniques have proven to be a powerful tool for continuous real-time monitoring and assessment of tropical reservoirs water quality. Although many studies have detected chlorophyll-a (Chl-a) concentrations as a proxy to represent nutrient contamination, using Sentinel 2 for eutrophic or hypereutrophic inland water bodies, mainly reservoirs, minimal eforts have been made for oligotrophic and mesotrophic reservoirs. The present study aimed to develop a modeling framework to map and estimate spatio-temporal variability of Chl-a levels and associated water spread using the Modifed Normalized Diference Water Index (MNDWI) and Maximum Chlorophyll Index (MCI). Moreover, the impact of land use/land cover type of the contributing watershed in the oligo-mesotrophic reservoir, Bhadra (tropical reservoir), for 2018 and 2019 using Sentinel 2 satellite data was analyzed. The results show that the water spread area was higher in the post-monsoon months and lower in the summer months. This was further validated by the correlation with reservoir storage, which showed a strong relationship (R2 = 0.97, 2018; R2 = 0.93, 2019). The estimated Chl-a spread was higher in the winter season, because the reservoir catchment was dominated by deciduous forest, producing a large amount of leaf litter in tropical regions, which leads to an increase in the level of Chl-a. It was found that Chl-a spread in the reservoir, specifcally at the inlet sources and near agricultural land practices (western parts of the Bhadra reservoir). Based on the fndings of this study, the MCI spectral index derived from Sentinel 2 data can be used to accurately map the spread of Chl-a in diverse water bodies, thereby ofering a robust scientifc basis for efective reservoir management.

@inproceedings{bib_Appl_2023, AUTHOR = {Kumar, Mummidivarapu Satish and Shaik, Rehana and Singh, Himanshi }, TITLE = {Application of QUAL2K Model for Water Quality Modeling of Bhadra River Stretch, India}, BOOKTITLE = {International Conference on Hydraulics, Water Resources and Coastal Engineering}. YEAR = {2023}}

This research was carried out along the Bhadra River, which is one of the tributaries of the Tungabhadra and originates near Gangamula in Karnataka’s the Western Ghats. The study stretch is around 27 km divided into three reaches with elements of 1 km as 3, 4, and 20 for each reach, respectively. The objectives of the study are to assess the effects of wastewater discharges on the water quality of the Bhadra River stretch and to simulate the dissolved oxygen (DO) by varying the biochemical oxygen demand (BOD) loads coming from different pollutant sources within the study stretch using the QUAL2K river water quality model. The study period considered is about 24 months (April 2014–March 2016) for the simulation of temperature, pH, DO, and nitrate parameters with the help of observed data at three monitoring stations. The investigation period was separated into two parts: the model was calibrated using monthly average data from April 2014 to March 2015 (12 months), and the model was validated using monthly average data from April 2015 to March 2016 (12 months). The R-square (R2) value for temperature and pH ranges from 0.87–0.56 to 0.64–0.62, respectively. The Root Mean Square Error (RMSE) for nitrate and DO ranges from 0.06–0.02 to 0.47–0.34, respectively. The results revealed that the Bhadra river stretch was highly polluted due to effluents coming from the industries present in the study stretch. It was also discovered that river flow rate, point source discharge, fast Carbonaceous Biochemical oxygen demand (CBOD) oxidation rate, and nitrification rate as the highly sensitive quality parameters defining the Bhadra river water quality. There must be a reduction of 25% of BOD effluent to reach the minimum standards set by the Central Pollution Control Board (CPCB). It is noted that a 75% reduction of BOD effluent from point sources will lead to an increase of 15% average DO throughout the study stretch.

@inproceedings{bib_Mapp_2023, AUTHOR = {Kumar, Mummidivarapu Satish and Shaik, Rehana and Rao, Y.R. Satyaji }, TITLE = {Mapping and assessment of river water quality under varying hydro-climatic and pollution scenarios by integrating QUAL2K, GEFC, and GIS}, BOOKTITLE = {Environmental Research}. YEAR = {2023}}

Water quality modelling has proved to be effective method for managing river water quality. But the most effective and comprehensive approach involving integration of river water quality simulation and pollution visualization with the objective of pollution reduction and maintenance of environmental flow strategies has gained less attention. Thus, the objective of this study was to employ an integrated approach for mapping and analysing river water quality under various hydro-climatic and pollution scenarios. Specifically, this approach involved the integration of a river water quality simulation model, QUAL2K, Global Environmental Flow Calculator (GEFC), and Geographical Information System (GIS) to develop water quality index (WQI) based map charts of water quality. The calibrated QUAL2K model was utilized to simulate WQI parameters including water temperature, pH, electrical conductivity, dissolved oxygen (DO), biological oxygen demand (BOD), nitrates (NO3), ammonia (NH4), and alkalinity. To analyse the WQI, the Weighted Arithmetic-Water Quality Index (WA-WQI) method was employed for various individual and combined pollution scenarios, environmental flow (Eflow), and climate change scenarios. The developed integrated approach was applied to the Bhadravati segment of Bhadra River, India. The findings revealed that the prevailing WQI status of the study stretch ranged from poor to unsuitable for drinking purposes. This deterioration can be attributed to the impact of both industrial and municipal effluents. By maintaining the effective Environmental Management Class (EMC) flow rates (class C flowrate of EMC (40.32 m3/s)) in conjunction with appropriate Pollution Reduction (PR) level (10% PR) at headwater and incoming drains, the stream self-purification capacity was enhanced resulting in the Bhadravati River stretch water quality transitioning to favourable water quality condition.

@inproceedings{bib_IoT__2023, AUTHOR = {Gandhi, Maulesh Tejas and Mathew, Ajai and Chaudhari, Sachin and Shaik, Rehana and Vattem, Anuradha }, TITLE = {IoT and ML-based Water Flow Estimation using Pressure Sensor}, BOOKTITLE = {India Council International Conference}. YEAR = {2023}}

This study presents an Internet of Things (IoT)- based system that utilises machine learning (ML) techniques to estimate water flow through pipes based on pressure. The system incorporates an ESP-32 microcontroller, a Danfoss MBS 3000 pressure sensor, and a flow meter deployed at three locations to collect data for three months. To model the relationship between pressure and flow rate, ML algorithms such as linear regression (LR), support vector regression (SVR), and convolutional neural network (CNN) were trained, analysed, and compared. By establishing a model to estimate the flow rate based on pressure, the need for a flow meter in the setup can be eliminated. The system’s low-cost, easy-to-implement, and non-invasive nature makes it suitable for widespread adoption in residential areas offering a promising solution for optimising water distribution and reducing water wastage.

@inproceedings{bib_Hydr_2023, AUTHOR = {Maddu, Rajesh and Mohan, G.Krishna and Shaik, Rehana }, TITLE = {Hydrological Drought Impacts on River Water Quality of Peninsular River System, Tunga-Bhadra River, India }, BOOKTITLE = {Integrated Drought Management}. YEAR = {2023}}

Drought is a natural hazard due to the lack of water availability in terms of precipitation and the consequent shortage of streamflow and soil moisture affecting the socioeconomics of a country. Although drought impacts on water quantity in terms of precipitation, streamflow, and soil moisture are widely recognized, the impacts on water quality are less known. Specifically, hydrological drought is observed to be impacted by climate change, which in turn probably increased the frequency and intensity of low river flows, affecting the water quality. Hydrological drought in combination with high water temperatures may deteriorate river water quality. This study analyzes hydrological drought impacts on river water quality for a peninsular river system, the Tunga-Bhadra River in India. A drought assessment has been done over the Tunga-Bhadra River Basin with a keen focus on studying the impact of hydrological drought on the water quality by observing the behavior of water quality parameters between the drought period and the reference years. The impact of hydrological drought on water quality during a drought period was assessed by calculating the Standardized Precipitation Index (SPI) and Streamflow Drought Index (SDI) for the studied period from June 1, 2005, to May 31, 2017. A statistical analysis has been done to study the significance of the impact on the water quality over the river basin. Time series of water quality parameters were investigated at three monitoring stations during the common drought period, occurring in the year 2013. A total amount of five water quality parameters were involved in the analysis: water temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), and nitrates. The behavioral changes of the parameters were studied, analyzed, and justified. An increase in river water temperature (3.4°C) with a decrease in discharge (25%) have resulted in lower DO (0.3 mg/l) values during the drought year of 2013 compared to the 2012 reference period along with the Bhadravathi station along the Bhadra River. A decrease in discharge during drought events along with an increase in river water temperature under an increase of air temperature resulted in the lowering of oxygen saturation concentrations and decrease in DO levels. The results indicate deterioration of river water quality of the Tunga-Bhadra River during drought, with respect to high water temperatures, reduction in dilution

@inproceedings{bib_Deve_2023, AUTHOR = {Kumari, Pallavi and Shaik, Rehana and Singh, Shailesh Kumar and Inayathulla, M. }, TITLE = {Development of a new agro-meteorological drought index (SPAEI-Agro) in a data-scarce region}, BOOKTITLE = {Hydrological Sciences Journal}. YEAR = {2023}}

Drought complexity may not be accurately characterized by univariate meteorological or hydrological drought indices under the intensification of hydrological cycle due to climate change and human activities. In particular, such drought indices require long series of hydro-meteorological data, which are unavailable over ungauged and data-scarce catchments. In this study, a multivariate drought index, Standardized Precipitation Actual Evapotranspiration Index-Agro (SPAEI-Agro), is proposed, which com- bines meteorological and hydrological variables as precipitation (P), actual evapotranspiration (AET), runoff and groundwater (GW) of ungauged catchments and sub-catchment scales with diverse climatol- ogy. SPAEI-Agro was able to characterize severe drought events more accurately in humid and dry sub- humid sub-catchments compared to Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Streamflow Drought Index (SSDI) for Tunga-Bhadra River, India. SPEI (based upon potential evapotranspiration) and SSDI (based on streamflow) showed intensified drought characteristics com- pared to the new P, AET and GW-based drought indicator SPAEI-Agro in semi-arid and dry sub-humid climates.

@inproceedings{bib_Timb_2023, AUTHOR = {Sah, Priyanka and Sharma, Subrat and Latwal, Avantika and Shaik, Rehana }, TITLE = {Timberline and Climate in the Indian Western Himalayan Region: Changes and Impact on Timberline Elevations}, BOOKTITLE = {Climate Change and Urban Environment Sustainability}. YEAR = {2023}}

In Himalayas, the high-elevational mountains have warmed more rapidly in recent decades than other areas of the globe. Himalayan timberline is climate dependent and sensitive to changes, thus can provide biological proof of global warming. The present analysis changes in timberline elevations over four decades and corresponding climatic parameters (temperature and precipitation) in the Western Himalayan region (Himachal Pradesh). Landsat-2 Multispectral Scanner (MSS) and Landsat-8 were used to evaluate the long-term (1976–2015) timberline dynamics. The climate data APHRODITE was used to calculate the annual mean temperature and annual precipitation along the spatially distinct timberline locations. The mean elevation of timberline position has shifted vertically 145 m with a rate of ~37 m per decade (@ 3.7 m per year) over the past four decades, however majority of timberline remained

@inproceedings{bib_Asse_2023, AUTHOR = {Shaik, Rehana and Maddu, Rajesh }, TITLE = {Assessment of Impacts of Climate Change on Indian Riverine Thermal Regimes Using Hybrid Deep Learning Methods}, BOOKTITLE = {Water Resources Research}. YEAR = {2023}}

Understanding the riverine thermal regimes is challenging due to sparse spatiotemporal data of river water temperatures (RWTs). The development of systematic models combined with machine learning models under data‐limited context has not been intensively studied for the prediction of RWT. The present study developed hybrid models using long short‐term memory (LSTM), integrated with (a) k‐nearest neighbor (k‐NN) bootstrap resampling algorithms (kNN‐LSTM) to address the data limitations of RWT prediction and (b) discrete wavelet transform (WT) approach (WT‐LSTM) to address the time–frequency localized features of RWT prediction. The study assessed the climate change impacts on RWT using an ensemble of National Aeronautics Space Administration Earth Exchange Global Daily Downscaled Projections of air temperature with Representative Concentration Pathway scenarios 4.5 and 8.5 for

@inproceedings{bib_Stud_2023, AUTHOR = {REDDY, MEEGADA V RAVI KISHORE and Mohanty, Supriya and Shaik, Rehana }, TITLE = {Study on effective utilization of coal ash in the conservation of natural resources and reduction of CO2 emissions}, BOOKTITLE = {innovative Infrastructure Solutions}. YEAR = {2023}}

The management and regulation of the waste generated from coal-based power plants have been a key issue throughout the world. There is a need for the voluminous utilization of coal ash in India. The bulk utilization of the industrial waste materials like coal ash can control the exploitation of the natural resources like soil in diferent aspects. Here, it has been attempted to evaluate the potential use of coal ash as a foundation/fll material. In addition, physical, geotechnical, morphological, and chemical characterization of the pond ash has been done. Then, fnite element modeling of the vibratory roller compaction was simulated using PLAXIS3D software for the recommendation of the pond ash as a construction material in place of soil. Also, the reduction in CO2 emission with pond ash was compared with that of the soil. The reduction in CO2 emission was found to be about 15% considering several factors like distance from the source station to the test site, types of coal ash used, types of construction equipment employed, etc. The utilization of pond ash as fll material was found to be economical, and its utilization saves around INR 32 to INR 399 per cubic meter. Keywords Coal ash · Foundation/fll material · Finite element modeling · Vibratory roller compaction · CO2 emission

@inproceedings{bib_Rela_2023, AUTHOR = {Latwal, Avantika and Sah, Priyanka and Sharma, Subrat and Shaik, Rehana }, TITLE = {Relationship Between Timberline Elevation and Climate in Sikkim Himalaya}, BOOKTITLE = {Ecology of Himalayan Treeline Ecotone}. YEAR = {2023}}

This study was undertaken to describe characteristics of climate (air temperature and precipitation) at timberline in relation to topography, and also bring out the changes in climate that have taken place over the past 37 years (1977–2015) in the Sikkim Himalaya. The rate of increase in mean air temperature was more at timberline elevations of the outer Himalayan ranges than the inner Himalayan ranges (0.21 °C decade−1 for island type timberline [ITL] vs 0.23 °C decade−1 for continuous type timberline [CTL]), and same was true for rainfall (more at elevations of the outer Himalayan timberline [ITL, 192 mm decade−1] than the elevations of the inner Himalayan timberline [CTL, 96 mm decade−1]). Elevational bands of similar elevation (2800–3500 m asl) in a timberline zone that were away from permanent snowline were 3.3–4.5 °C warmer than the same elevation close to permanent snowline (inner ranges), which received less rainfall (438–650 mm) than the outer ranges. Annual mean air temperature was higher (~1 °C) in the locations where timberline moved upwards in 37 years. It was observed that the outer Himalayan ranges became warmer and wetter than the inner Himalayan timberline in the studied period. The warming rate was more at island type timberline (ITL) sites than continuous type timberline (CTL) sites indicating that inner regions in Sikkim are able to hold their own climatic features compared to CTL areas that are more vulnerable or responsive to climate change.

@inproceedings{bib_Impr_2022, AUTHOR = {Maddu, Rajesh and Sachdeva, Anishka and Pansari, Satyam Viksit and Srivastav, Arohi and Shaik, Rehana }, TITLE = {Improving Short‑range Reservoir Inflow Forecasts with Machine Learning Model Combination}, BOOKTITLE = {Water Resources Management}. YEAR = {2022}}

This paper presents a simple and effective framework to combine various data-driven machine learning (ML) algorithms for short-range reservoir inflow forecasting, including the large-scale climate phenomenon indices addressing forecasting uncertainty. Random Forest (RF), Gradient Boosting Regressor (GBR), K-Nearest Neighbors Regressor (KNN), and Long Short-Term Memory (LSTM) were employed for predicting daily reservoir inflows considering various climate phenomenon indices (e.g., Arctic Oscillation, North Atlantic Oscillation, and Southern Oscillation Index) and hydroclimatic variables (e.g., precipitation), accounting for time-lag effects. After training the individual ML algorithm, a framework was developed to create an ensemble model using a robust weighted voting regressor (VR) method to quantify forecasting uncertainty and to improve model perfor- mances. The results of the study reveal that, for 2-day forecasts, the LSTM approach has the greatest influence on prediction accuracy, followed comparably by each model. How- ever, none of the four models seem to be noticeably superior to the VR method, regardless of the prediction lead time. The developed framework was examined on a tropical reser- voir, Bhadra reservoir Tunga-Bhadra River, located in India.

@inproceedings{bib_Wate_2022, AUTHOR = {Srivastav, Arohi and Kumar, Mummidivarapu Satish and Shaik, Rehana }, TITLE = {Water Quality Index Prediction using Regression for Bhadra River}, BOOKTITLE = {International Conference on Hydraulics, Water Resources and Coastal Engineering}. YEAR = {2022}}

Rivers are a significant and low-cost supply of irrigation, industrial, and residential water across the world. Due to urbanization, population growth, and climate change, many rivers are now confronting an irreparable water crisis. According to a study carried out in 2018 by the Central Pollution Control Board (CPCB) of India, there are a total of 351 polluted river stretches in India. Accessing the pollution level in these rivers is a challenge and requires the measurement of several water quality indices including turbidity, coliform levels, total phosphate, etc. These indices are then used to calculate the water quality index which gives us a fair estimate of the level of pollution in water. However, the measurement of all these indices is challenging and requires the use of complex measuring tools. In this paper, we try to solve this problem by building machine learning models that can predict the water quality index just by using a subset of the indices that are required for the calculation of the water quality index. The models trained take turbidity, pH, Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), total coliform, hardness as CaCo3, fluoride, ammonia, Chemical Oxygen Demand (COD) and total suspended solids as input and make predictions for water quality using regression techniques. The models are trained specifically for Bhadra river data, but the technique can be extended for other river stretches and water bodies. We use mean squared error, mean absolute error and R2 score to evaluate our models. An artificial neural network trained with 7 dense layers outperforms other regressors in terms of the evaluation metrics used. With the availability of better datasets, the technique of using regressors for the prediction of water quality could help in the better and faster assessment of water quality which can eventually lead to quicker remedial measures

@inproceedings{bib_PERF_2022, AUTHOR = {VISHAL, VEERANNAPET SANTHOSH and Shaik, Rehana and Munilakshmi, N. and Praveen, L Partha }, TITLE = {PERFORMANCE ANALYSIS OF 339 MLD SEWAGE TREATMENT PLANT AT AMBERPET – HYDERABAD}, BOOKTITLE = {conference on International Society of Waste Management, Air and Water}. YEAR = {2022}}

PERFORMANCE ANALYSIS OF 339 MLD SEWAGE TREATMENT PLANT AT AMBERPET – HYDERABAD. Veerannapet Santhosh Vishal1, Shaik Rehana2, N. Munilakshmi3, L Partha Praveen4 1- Research Scholar, International Institute of Information Technology, Gachibowli, Hyderbad, Telangana-500032. 2- 2- Assistant Professor, International Institute of Information Technology, Gachibowli, Hyderbad, Telangana - 500032. 3- 3- Assistant Professor, Department of Civil Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh – 517502. 4- 4- Research Scholar, Department of Civil Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh – 517502 * Corresponding author Email: santhoshvishal.v@research.iiit.ac.in Abstract Environmental and water resources have deteriorated due to rapid population growth, chemical industry effluents, agricultural practices, and climate changes. The primary cause of water contamination is due to sewage water pollution. Hence understanding and implementing an effective sewage treatment is necessary. Many cutting-edge techniques have been developed in recent years to increase the effectiveness of the removal of organic matter and nutrients by wastewater treatment plants (WWTP). To comprehend the effectiveness of wastewater treatment, the current study considered a sewage treatment plant (STP) located in Amberpet, Hyderabad. The capacity of the STP at Amberpet is 339 MLD (Million Litres per Day) and is evaluated by collecting 156 samples for 12 months (January 2018 – December 2018). An STP aims to minimize or remove organic debris, sediments, disease-causing organisms, and other pollutants in sewage water before disposing into the streams. The current study observed the removal efficiencies of the constituents Total Suspended Solids (TSS), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD). This investigation assesses whether the effluents emitted into the river body are within the NRCD- set limitations (National River Conservative Directorate) as the treated sewage water is discharged into the Musi River (a tributary of Krishna Basin). The MSE values of different parameters like TSS, BOD, COD are 23.85, 28.54, 72.59. The RMSE values of TSS, BOD, COD are 4.88, 5.32, 8.52. The R2 values of TSS, BOD, COD are 0.95, 0.79, 0.90. Keywords: Wastewater Treatment Plant (WTTP), Biochemical Oxygen Demand, Chemical Oxygen Demand, Total suspended solids

@inproceedings{bib_EFFE_2022, AUTHOR = {VISHAL, VEERANNAPET SANTHOSH and Maddu, Rajesh and Shaik, Rehana }, TITLE = {EFFECTS ON DISSOLVED OXYGEN SATURATION AND WATER TEMPERATURE USING AIR2STREAM OVER KRISHNA RIVER BASIN, INDIA.}, BOOKTITLE = {International Conference on Hydraulics, Water Resources and Coastal Engineering}. YEAR = {2022}}

River water temperature signifies the health of the river body and regulates many physical and chemical parameters related to river water quality parameters, which speculatively depend on many factors. So far, most river water temperature models are either physical or data-driven models requiring large amounts of hydrological and meteorological observations. Many climate change studies have been conducted in relation to increasing stream water temperatures, but how it affects saturated dissolved oxygen levels have not been addressed. To address these, the present work aims to work with a hybrid model - Air2Stream as a function of air temperature and discharge and also demonstrates how the Air2Stream method can be used to generate accurate RWT predictions and subsequent DO concentrations in river water quality modeling. The Air2Stream model can be used to forecast river water temperature which combines the concepts of the heat budget equation that generalizes the physical processes and infers links between input and output data. The proposed modeling framework's effectiveness is demonstrated with three river gauging stations of Mantralayam, Shimoga and Keesara in the Krishna River basin. Model performance was evaluated by comparing simulated river water temperature time series with corresponding observations using the Nash-Sutcliffe efficiency (NSE) and Root Mean Square Error (RMSE) coefficients. Model results show that for all study sites, NSE values range from 0.76 to 0.97 for

@inproceedings{bib_Asse_2022, AUTHOR = {Singh, Himanshi and Shaik, Rehana and Kumar, Mummidivarapu Satish }, TITLE = {Assessment of River Water Quality Using Qual2k Model and Water Quality Index for the Bhadra River, India}, BOOKTITLE = {International Conference on Hydraulics, Water Resources and Coastal Engineering}. YEAR = {2022}}

Maintaining the river water quality has become more prominent these days due to excess pollutants from various sources like industries and domestic sewage discharge. The assessment of river water quality is a significant task for the management and sustainability of the riverine health system for future generations. This study evaluates the water quality of the Bhadra river stretch in India using the water quality index and qual2k model. The water quality index (WQI) is estimated using Weighted Average Water Quality Index (WAWQI) method and water quality indicators like Biochemical Oxygen Demand (BOD), Dissolved Oxygen (DO), Electrical Conductivity (Ec), Nitrate (NO3), pH, and Temperature are simulated using Qual2k model. The Qual2k model was calibrated and validated for a period of 91-months (April 2006 to October 2013). The estimated WQI values range from 92.35 to 112, and the quality classification ranges from very poor to unfit for consumption. It was observed that the quality status of the river water was very poor in upstream and downstream segments, while unfit for consumption in the middle segment indicating access to industrial and anthropogenic activities. To conserve river water from pollution, strict laws and regulations must be implemented to improve health and preserve water resources for future generations.

@inproceedings{bib_Envi_2022, AUTHOR = {Kumar, Mummidivarapu Satish and Priya, Palugulla Naga Chandi and Shaik, Rehana and Singh, Shailesh Kumar }, TITLE = {Environmental Flows Allocation for a Tropical Reservoir System by Integration of Water Quantity (SWAT) and Quality (GEFC, QUAL2K) Models}, BOOKTITLE = {Water Resources Management}. YEAR = {2022}}

Environmental flow (Eflow) allocations of reservoir-river systems do not adequately address the connections between reservoir inflows, releases, and corresponding downstream river water quality rendering water resources management difficult. The study integrated the Soil and Water Assessment Tool (SWAT) for reservoir inflows estimation and corresponding releases with Global Environmental Flow Calculator (GEFC) for Eflows allocations to simulate the resulting Dissolved Oxygen (DO) of the downstream river stretch using QUAL2K. The study considered various plausible scenarios of inflows (10 to 20% reduction), pollution scenarios of Biological Oxygen Demand (BOD) (0 to 100% treatment) for arriving drains by altering headwaters DO (4 to 8 mg/l). Eflow water quality charts were developed for the regulation of reservoir downstream river water quality for Bhadra river in India as case study. The study revealed that by maintaining the headwater DO in conjunction with BOD treatment of drains and with sufficient Eflow allocation, downstream river water quality can be improved. By maintaining a headwater DO of 7 mg/l with BOD treatment of drains of 25% and by providing Eflow allocation of 40.12 m3/s with class D, the Bhadravathi river stretch has shown an improved river water quality with about 6.65 mg/l of average DO.

@inproceedings{bib_Impr_2022, AUTHOR = {Maddu, Rajesh and Anishka, Sachdeva and Viksit, Pansari Satyam and Arohi, Srivastav and Shaik, Rehana }, TITLE = {Improving Short-range Reservoir Inflow Forecasts with Machine Learning Model Combination}, BOOKTITLE = {Water Resources Management}. YEAR = {2022}}

This paper presents a simple and effective framework to combine various data-driven machine learning (ML) algorithms for short-range reservoir inflow forecasting, including the large-scale climate phenomenon indices addressing forecasting uncertainty. Random Forest (RF), Gradient Boosting Regressor (GBR), K-Nearest Neighbors Regressor (KNN), and Long Short-Term Memory (LSTM) were employed for predicting daily reservoir inflows considering various climate phenomenon indices (e.g., Arctic Oscillation, North Atlantic Oscillation, and Southern Oscillation Index) and hydroclimatic variables (e.g., precipitation), accounting for time-lag effects. After training the individual ML algorithm, a framework was developed to create an ensemble model using a robust weighted voting regressor (VR) method to quantify forecasting uncertainty and to improve model performances. The results of the study reveal that, for 2-day forecasts, the LSTM approach has the greatest influence on prediction accuracy, followed comparably by each model. However, none of the four models seem to be noticeably superior to the VR method, regardless of the prediction lead time. The developed framework was examined on a tropical reservoir, Bhadra reservoir Tunga-Bhadra River, located in India

@inproceedings{bib_Impa_2022, AUTHOR = {Maddu, Rajesh and K.M, Ganta and Shaik, Rehana and C.T., Dhanya }, TITLE = {Impact Assessment of Environmental Flows Using CORDEX Regional Climate Models: Case Study of Nagarjuna Sagar Dam, Krishna River, India}, BOOKTITLE = {Advanced Modelling and Innovations in Water Resources Engineering}. YEAR = {2022}}

mpact Assessment of Environmental Flows Using CORDEX Regional Climate Models: Case Study of Nagarjuna Sagar Dam, Krishna River, India

@inproceedings{bib_Comp_2022, AUTHOR = {Reddy, M. V. Ravi Kishore and Mohanty, Supriya and Shaik, Rehana }, TITLE = {Comparative Study on Static and Seismic Performance of Ash-Foundation System}, BOOKTITLE = {IndianYoung Geotechnical Engineers Conference}. YEAR = {2022}}

Utilization of coal ash under a foundation of structure deals with huge quantity of its usage that solves the issue of its disposal, which is trivial in most parts of the world. Adaptability of coal ash under foundation needs to be examined on a large scale. In this study, it is attempted to investigate static and seismic response of shallow foundation resting on pond ash deposit. Two-dimensional (2D) response analysis is carried out for the ash-foundation system by employing finite element software PLAXIS 2D. It has been recognized since decades that soil-foundation interaction or ash-foundation interaction is prime factor in the seismic response of structure or portions of structure. Ash-foundation system is excited under the influence of Nepal earthquake motion (Mw: 7.8) and North East India earthquake motion (Mw: 7.5). The displacement was observed more in case of North East India earthquake, i.e. 6.028 m. However, acceleration and excess pore pressure response were high in case of Nepal earthquake, i.e. 0.152 g and 129.16 kPa, respectively. These responses can be utilized to assess the performance of ash-foundation system.

@inproceedings{bib_Envi_2022, AUTHOR = {Maddu, Rajesh and Shaik, Rehana and Dhanya, C. T. }, TITLE = {Environmental Flow Impacts on Water Quality of Peninsular River System: Tunga-Bhadra River, India}, BOOKTITLE = {Riverine Systems}. YEAR = {2022}}

Environmental flows play a major role in terms of quantity and quality for sustainable riverine ecosystems (Brisbane Declaration, 2007). Environmental Flows (EFs) have a variety of impacts in different regions of the world, including fisheries and other aquatic life, assimilative capacity, drinking water security, agriculture, transportation, navigation, industry, flood protection, recreation and tourism, and other cultural aspects (Iyer, 2005). The EFs are a measure of the amount and quality of water flowing in a freshwater river or stream over time. Estimation of EFs should be able to consider hydrologic, hydraulic, habitat and biodiversity, water quality, socioeconomic and cultural aspects with consideration of water regulation policies (Tennant, 1976). Estimation of EFs is generally practiced with consideration of one or multiple factors. Among these, the hydrological aspect with consideration of historical natural flow data is the most common and practiced by several river water management stakeholders (Zeiringer et al., 2018). Environmental flows based on hydrological criteria were conventionally estimated using flow indices based on a selected threshold level, which may be different for various river systems (Sharma and Dutta, 2020).

@inproceedings{bib_Shor_2022, AUTHOR = {Maddu, Rajesh and Pradhan, Indranil and , Ebrahim Ahmadisharaf and Singh, Shailesh Kumar and Shaik, Rehana }, TITLE = {Short-range reservoir inflow forecasting using hydrological and large-scale atmospheric circulation information}, BOOKTITLE = {Journal of Hydrology}. YEAR = {2022}}

Short and long range reservoir inflow forecast is essential for efficient real time operational planning, scheduling of hydroelectric power system and management of water resources. Large-scale climate phenomenon indices have a strong influence on hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. This study aims to explore the relevance of large-scale climate phenomenon indices in improving the reservoir inflow prediction at short-term time scales. This paper presents a simple and effective framework to combine various data-driven machine learning (ML) algorithms for short-range reservoir inflow forecasting. Random Forest (RF), Gradient Boosting Regressor (GBR), K-Nearest Neighbors Regressor (KNN), and Long Short-Term Memory (LSTM) were employed for predicting daily reservoir inflows considering various climate phenomenon indices (e.g., Arctic Oscillation, North Atlantic Oscillation, and Southern Oscillation Index) and hydroclimatic variables (precipitation), accounting for time-lag effects. After training the individual ML algorithm, a framework was developed to create an ensemble model using a robust weighted voting ensemble method to quantify forecasting uncertainty and to improve the model performance by combining the inflow results of the single ML model and the highest vote is chosen based on the weights assigned to the single ML model. The developed framework was examined in two distinct reservoirs located in India and California, USA. The ensemble model consistently outperformed the standalone RF, GBR, KNN, and LSTM in predicting high (flood control and monsoon seasons) and low flows (runoff and non-monsoon seasons) of both study reservoirs. The demonstrated short-term reservoir forecasting model allows reservoir operators to adapt and add regional hydrological and large-scale climate indices in real-time decision-making. The presented framework can be applied for any reservoir inflow forecasting. Introduction Reservoirs are important hydraulic structures in terms of collection, storage, and diversion of freshwater for purposes such as drinking, irrigation, hydropower, flood management operations, and drought relief (Simonovic, 2020). Many developing countries (such as India) have made efforts towards sustainable reservoir operations to account for the adverse effects of dams for regional water resources management (Bhadoriya et al., 2020, Goel et al., 2020, Rehana and Mujumdar, 2014). Reservoir inflows play a major role in water allocations fulfilling various water demands and balancing the flows from upstream catchments to downstream regions during floods (Rehana et al., 2020). The operation of reservoir is complex, involving multiple time scales, multi-flow regimes, and unpredictable emergencies (Zhang et al., 2018). Forecasting reservoir inflows is a preliminary steps in reservoir operation, providing guidelines and rule curves for optimal water allocations to satisfy water supply, irrigation, industrial, hydropower, and environmental conservation requirements (Kasiviswanathan et al., 2020). In this context, long and short-term reservoir inflow forecasts are vital for sustainable water resources planning and management (Rehana et al., 2020). Short-term seasonal and monthly operations are relevant for optimal economic benefits, water supply, and downstream augmentation operations (Zhang et al., 2018). Daily time scale operations also relate to controlling floods, power grid loads, and emergency operations (Noorbeh et al., 2020). In this context, the prediction of daily reservoir inflows is prominent for real-time reservoir operation under hydroclimatic extremes such as low and high flows. Conventionally, real-time reservoir operations are associated with high variability and rapid changes, often deviating from the operation rule curves developed based on physical models (Oliveira & Loucks, 1997). Further, physically based models are subject to various uncertainties and need to account for complex natural and human-influenced hydrological conditions and water demands. Although hydrological models are best tools for predicting inflows, and providing details of precipitation, evapotranspiration, soil, and land use characteristics of the upstream catchment area, these models require a rigorous amount of landscape information at various spatiotemporal scales and several pre-processing efforts (Awol et al., 2019). Alternatively, data-driven algorithms based on Machine Learning (ML)

@inproceedings{bib_Impa_2022, AUTHOR = {Maddu, Rajesh and Shaik, Rehana }, TITLE = {Impact of climate change on river water temperature and dissolved oxygen: Indian riverine thermal regimes}, BOOKTITLE = {Scientific Reports}. YEAR = {2022}}

The impact of climate change on the oxygen saturation content of the world’s surface waters is a signifcant topic for future water quality in a warming environment. While increasing river water temperatures (RWTs) with climate change signals have been the subject of several recent research, how climate change afects Dissolved Oxygen (DO) saturation levels have not been intensively studied. This study examined the direct efect of rising RWTs on saturated DO concentrations. For this, a hybrid deep learning model using Long Short-Term Memory integrated with k-nearest neighbor bootstrap resampling algorithm is developed for RWT prediction addressing sparse spatiotemporal RWT data for seven major polluted river catchments of India at a monthly scale. The summer RWT increase for Tunga-Bhadra, Sabarmati, Musi, Ganga, and Narmada basins are predicted as 3.1, 3.8, 5.8, 7.3, 7.8 °C, respectively, for 2071–2100 with ensemble of NASA Earth Exchange Global Daily Downscaled Projections of air temperature with Representative Concentration Pathway 8.5 scenario. The RWT increases up to7 °C for summer, reaching close to 35 °C, and decreases DO saturation capacity by 2–12% for 2071–2100. Overall, for every 1 °C RWT increase, there will be about 2.3% decrease in DO saturation level concentrations over Indian catchments under climate signals.

@inproceedings{bib_Prec_2022, AUTHOR = {Shaik, Rehana and Yeleswarapu, Pranathi and BASHA, GHOUSE and MUNOZ-ARRIOLA, FRANCISCO }, TITLE = {Precipitation and temperature extremes and association with large-scale climate indices: An observational evidence over India}, BOOKTITLE = {Journal of Earth System Science}. YEAR = {2022}}

Climate change exposes more frequent natural hazards and physical vulnerabilities to the built and natural environments. Extreme precipitation and temperature events will have a significant impact on both the natural environment and human society. However, it is unclear whether precipitation and temperature extremes increase physical vulnerabilities across scales and their links with large-scale climate indices. This study investigates the relationship between precipitation and temperature extremes, as recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), and large scale climatological phenomenon indices (Indian Summer Monsoon Index (ISMI), Arctic Oscillation (AO), and North Atlantic Oscillation (NAO)), using India as a case study. Our Bndings show that extreme warm indices were primarily negatively related to ISMI and positively related to extreme cold indices. According to Pearson’s correlation coefBcients and Wavelet Transform Coherence (WTC), extreme warm indices were negatively related to ISMI and positively related to extreme cold indices. The extreme precipitation indices had a significant positive relationship, primarily with AO. Furthermore, from 1951 to 2018, India experienced an increase in warm extremes over western, central, and peninsular India, while cold indices increased over northwest India. Precipitation extremes of more than one day, more than Bve days, very wet and extremely wet days have increased across India except in the Indo-Gangetic plains, while heavy and very heavy precipitation days, consecutive wet days, and consecutive dry days have decreased.

@inproceedings{bib_Mach_2022, AUTHOR = {Ayaz, Adeeba and SHARATHCHANDRA, VANNAM and Singh, Shailesh Kumar and Shaik, Rehana }, TITLE = {Machine Learning Models for Estimating Actual Evapotranspiration with Limited Data}, BOOKTITLE = {Sustainable Environment Research}. YEAR = {2022}}

The present study compared various empirical and data-driven algorithms to predict Actual Evapotranspiration (AET) using various hydro climatic variables. The AET over semi-arid climatic conditions of Hyderabad, Telangana, India, and Waipara (New Zealand) was estimated using different empirical methods-based PET using Budyko and Turc models. Modelled PET from five data-driven algorithms, such as Long short-term memory neural networks (LSTM), Artificial Neural Network (ANN), Gradient Boosting Regressor, Random Forest, and Support Vector Regression were trained to predict AET using meteorological variables. The results show simple empirical-based AET models, Budyko and Turc, can estimate AET very well. The results indicated that 99% accuracy could be achieved with all climatic input, whereas accuracy drops to 86% with limited data. Both LSTM and ANN models based on PET have been noted as the most robust models for estimating AET with minimal climate data. It was observed that the meteorological variables of temperature and solar radiation have more significant contributions than other variables in the estimation of AET. In addition, the effects of the meteorological variables were found to be essential and effective in the estimation of AET. The research findings of the study reveal that under limited data availability, the best input combinations were identified as temperature and wind speed for estimating PET; temperature, wind speed, and precipitation for estimating AET for semi-arid climatology.

@inproceedings{bib_Asse_2022, AUTHOR = {Priya, Palugulla Naga Chandi and Shaik, Rehana and Singh, Rahul Kumar }, TITLE = {Assessment of Environmental Flows of Tungabhadra River Using Global Environmental Flow Calculator}, BOOKTITLE = {Innovative Trends in Hydrological and Environmental Systems}. YEAR = {2022}}

Maintaining the full scale of naturally occurring river flow is usually impossible due to the development of the water resources and variations of land and soil usage in the catchment. These developed resources can create differences in the balance of the ecosystem and socio-economic activities. These designed structures also cause a decrease in the minimum flow regime in downstream. Environmental flows (EFs) are the medium that help maintain river flow in healthy or ecological conditions. The river’s hydrologic (river mapping), hydraulic (cross-section, water depth, and velocity), and environmental conditions (riparian flora and fauna) are significant considerations for estimating environmental flows (EF’s). There are various desktop assessment methods for calculating the environmental flows. In this present study, the global environmental flow calculator (GEFC) method is used to estimate the ecological flows by using the flow duration curves (FDC) generated from the given monthly discharge data of the river. The FDC in this system contains 17 fixed percentile points concerning the discharge. In the current study, we analyze the environmental flows of the Tungabhadra River basin by considering the different discharge stations, which are Balehonuur, Haralahalli, Hosaritti, Shivamogga, Honalli, Rattihalli, and Tungabhadra Dam, with a mean annual flow (MAF) of 36%, 24.8%, 27.2%, 16.2%, 23.3%, 21.1%, and 12.2%, respectively, to maintain the ecological conditions of the river. The monthly discharge data from 1995 to 2017 for those stations are obtain from the Advance Center for Integrated Water Resource (ACIWR) Bengaluru, India. The river flow health is a study which helps in understanding the environmental variables that effects the habitat structure, flow regime, water quality, and biological conditions of the river. To estimate the Flow Health of Tungabhadra River, we used a tool called Flow Health which uses nine indicators to represent the Flow Health (FH) score for the stations Balehonnur, Haralahalli, Hosaritti, Shivamogga, Honalli, Rattihalli, and Tungabhadra. This tool uses the gauge discharge data in the form of reference (1995–2005) and test periods (2006–2017), with Flow Health score of 0.72, 0.4, 0.72,0.70, 0.58, 0.73, 0.71 and 0.72, 0.63, 0.63, 0.7, 0.66, 0.67, 0.66 for test and reference period with respect to stations. The study noted that majority of the discharge stations along the Tungabhadra River show a moderate to low flow variations for the reference and test periods. Overall, Tungabhadra river health, measured by the flow indices, had declined from 1995–2005 to 2006–2017.

@inproceedings{bib_Asse_2022, AUTHOR = {Kumari, Pallavi and SHARATHCHANDRA, VANNAM and Shaik, Rehana and Inayathulla, M. }, TITLE = {Assessment of Meteorological Drought Using Standardized Precipitation Evapotranspiration Index—Hyderabad Case Study}, BOOKTITLE = {Climate Change and Water Security}. YEAR = {2022}}

Drought, a natural disaster, occurs in all types of climate. Temperature, wind, relative humidity, rainfall intensity and rainfall duration play a significant part in the phenomenon of droughts. It affects natural sources of water and also can reduce the water supply, quality of water, crop yield, as well as the economy and social activities. Drought is an extreme event that affects natural resources, environment and society for a long time. Usually, drought occurs gradually; but it can be more terrible than floods. Meteorological, hydrological and agricultural are its broad types as described in Fig. [1]. Out of these, meteorological drought, defined as an abnormal shortage of precipitation, is the main cause of all other types of droughts. Drought index measures the levels of drought by acquiring data into a single numerical value from one or more hydrological variables. Several drought indices are found to assess the change in climatic variables. Those are Palmer Drought Severity Index, Crop Moisture Index, Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and Reconnaissance Drought Index (RDI) [1]. Based on the drought studies, almost all drought indices use precipitation as the sole variable or with the other hydrological elements, as per the type of demand, which is indicated by WMO [2]. There have been many attempts over the last few decades to develop a new drought index on the basis of a climatological precipitation study [1, 3, 4]. Vicente-Serrano et al. [5] proposed a new evapotranspiration-based index which is Standardized Precipitation Evapotranspiration Index (SPEI) based on the calculation of Potential Evapotranspiration.

@inproceedings{bib_Obse_2022, AUTHOR = {Khan, Pathan Imran and Ratnam, Devanaboyina Venkata and Prasad, Perumal and Basha, Ghouse and Jiang, Jonathan H. and Shaik, Rehana and 2, Madineni Venkat Ratnam and Kishore, Pangaluru }, TITLE = {Observed Climatology and Trend in Relative Humidity, CAPE, and CIN over India}, BOOKTITLE = {Atmosphere}. YEAR = {2022}}

Water vapor is the most dominant greenhouse gas in the atmosphere and plays a critical role in Earth’s energy budget and hydrological cycle. This study aims to characterize the long-term seasonal variation of relative humidity (RH), convective available potential energy (CAPE), and convective inhibition (CIN) from surface and radiosonde observations from 1980–2020. The results show that during the monsoon season, very high RH values are depicted while low values are depicted during the pre-monsoon season. West Coast stations represent large RH values compared to other stations throughout the year. Irrespective of the season, the coastal regions show higher RH values during monsoon season. Regardless of season, the coastal regions have higher RH values during the monsoon season. During the pre-monsoon season, the coastal region has high RH values, whereas other regions have high RH values during the monsoon season. The rate of increase in RH in North-West India is 5.4%, followed by the West Coast, Central, and Southern parts of India.. An increase in water vapor leads to raised temperature, which alters the instability conditions. In terms of seasonal variation, our findings show that CAPE follows a similar RH pattern. CAPE increases sharply in Central India and the West Coast region, while it declines in South India. Opposite features are observed in CIN with respect to CAPE variability over India. The results of the study provide additional evidence with respect to the role of RH as an influencing factor for an increase in CAPE over India.

@inproceedings{bib_Appl_2021, AUTHOR = {, Mummidivarapu Satish Kumar and Shaik, Rehana and Singh, Himanshi }, TITLE = {Application of QUAL2K model for water quality modelling of Bhadra river stretch, India}, BOOKTITLE = {International Conference on Hydraulics, Water Resources and Coastal Engineering}. YEAR = {2021}}

This research was carried out along the Bhadra River, which is one of the tributaries of the TungaBhadra and originates near Gangamula in Karnataka’s the Western Ghats. The study stretch is around 27 km divided into three reaches with elements of 1 km as 3, 4, and 20 for each reach, respectively. The objectives of the study are to assess the effects of wastewater discharges on the water quality of the Bhadra River stretch, to simulate the Dissolved oxygen (DO) by varying the Biochemical oxygen demand (BOD) loads coming from different pollutant sources within the study stretch using the QUAL2K river water quality model. The study period considered is about 24 months (April 2014-March 2016) for the simulation of temperature, pH, DO, and Nitrate parameters with the help of observed data at three monitoring stations. The investigation period was separated into two parts: the model was calibrated using monthly average data from April 2014 to March 2015 (12 months), and the model was validated using monthly average data from April 2015 to March 2016 (12 months). The R-square (R2 ) value for temperature and pH ranges from 0.87-0.56 and 0.64-0.62, respectively. The Root Mean Square Error (RMSE) for Nitrate and DO ranges from 0.06-0.02 and 0.47-0.34, respectively. The results revealed that the Bhadra river stretch was highly polluted due to effluents coming from the industries present in the study stretch. It was also discovered that river flow rate, point source discharge, fast Carbonaceous Biochemical oxygen demand (CBOD) oxidation rate, and nitrification rate as the highly sensitive quality parameters defining the Bhadra river water quality. There must be a reduction of 25% of BOD effluent to reach the minimum standards set by the Central Pollution Control Board (CPCB). It is noted that a 75% reduction of BOD effluent from point sources will lead to an increase of 15% average DO throughout the study stretch. Keyword Qual2k model, Water quality, Bhadra River, Dissolved oxygen, BOD.

@inproceedings{bib_Mode_2021, AUTHOR = {Ayaz, Adeeba and SHARATHCHANDRA, VANNAM and PARAS, MANDLECHA PRATIK and Shaik, Rehana }, TITLE = {Modelling of Reference Evapotranspiration for Semi-arid Climates Using Artificial Neural Network}, BOOKTITLE = {}. YEAR = {2021}}

Reference Evapotranspiration (ET0) is one of the prominent hydrologic variables affecting water and energy balances and critical factors for crop water requirements and irrigation scheduling. evapotranspiration is a complex hydrological variable defined by various climatic variables. Various empirical formulations have been developed to estimate ET0 depending upon the availability of meteorological variables. Such empirical formulations are region-specific and are for particular climatic conditions. In this context, mathematical models have emerged as simple and readily implementable for the estimation of ET0 with measured meteorological parameters as independent variables. Such data-driven models can be valuable to predict ET0 when climate data is insufficient. The present study compared various empirical models and data-driven algorithms to predict ET0 using various climate variables. Artificial neural networks (ANN) were adopted to estimate reference ET0. Four empirical methods Penman-Monteith, Hargreaves, Turc, and Priestley-Taylor were used to estimate ET0 at a daily time scale. Dataset consists of daily meteorological data over a period of 51 years (1965–2015) for Hyderabad, the largest city of the Indian state, Telangana, with semi-arid climate. The input variables for the ANN model consist of maximum and minimum air temperatures, relative humidity, solar radiation, and wind speed. The Penman-Monteith method was considered as the standard method to compare the ANN and various empirical models of ET0. ANN model was trained and tested with climate variables as input variables and various empirical models as reference models. The most influencing climate variables on ET0 were found in the order of temperature, solar radiation, wind speed, and relative humidity based on correlation coefficients. These variables have formed as the basis to choose different datasets to train over ANN model. Validation has been carried out using the coefficient of determination (R2) which is obtained for the training (1965–2000) and testing period (2001–2015) period as 0.97 and 0.96 respectively. Temperature and radiation-based models of Turc and Priestley-Taylor methods can be used to estimate ET0 when all other climate variables are not available as they also correlate well with the Penman-Monteith method. Advancement towards artificial intelligence techniques in water resources engineering has motivated to simulate reference ET0 using limited meteorological variables to produce accurate results. Such data-driven algorithms developed based on standard empirical models can be implemented for prediction with limited climate data.

@inproceedings{bib_Asse_2021, AUTHOR = {Kumari, Pallavi and SHARATHCHANDRA, VANNAM and Shaik, Rehana and Inayathulla, M. }, TITLE = {Assessment of Meteorological Drought using Standardized Precipitation Evapotranspiration Index - Hyderabad Case Study}, BOOKTITLE = {Conference on Disaster Risk Reduction}. YEAR = {2021}}

Droughts are recognized as a natural disaster that is caused by extreme and continuous shortage of precipitation. Drought indices assist in a number of tasks, including its early warning and monitoring by computing severity levels and proclaiming the start and end of drought. Various drought indices were formulated for the forecasting and prediction of spatiotemporal drought characteristics using various hydrological variables, such as precipitation, evapotranspiration, runoff, soil moisture content, etc. Due to anthropogenic global warming and increase of temperature, evapotranspiration based drought indices have become interest in recent years in the drought assessment. This paper attempt to provide more information on drought indices which incorporates Evapotranspiration. The study used Standardized Precipitation Evapotranspiration index (SPEI), based on evapotranspiration to understand the drought variability at various time scales. This study adopted Hargreaves model to calculate Potential Evapotranspiration. SPEI index can also be used to study the wet and dry periods including Evapotranspiration along with precipitation. The study used SPEI to understand the dry and wet years over an urban semi-arid region, Hyderabad, capital and biggest city of the southern Indian state of Telangana for the year 1965 to 2015. The years 1965, 1966, 1972, 1973, 1985, 1993 and 2012 were noted as dry years with SPEI values as -1.35,-1.06,-1.43,-1.31,-1.05, -1.22 and -1.51 sequentially and year 2006 as severe wet year with SPEI value as +1.65. The characterization of dry and wet years as demonstrated in the present study will enhance the better urban water resources management.

@inproceedings{bib_Pred_2021, AUTHOR = {Maddu, Rajesh and Reddy, Vanga Abhishek and Kumar, Sajja Jashwanth and Basha, Ghouse and Shaik, Rehana }, TITLE = {Prediction of land surface temperature of major coastal cities of India using bidirectional LSTM neural networks}, BOOKTITLE = {JOURNAL OF WATER AND CLIMATE CHANGE}. YEAR = {2021}}

Surface Temperature (ST) is important in terms of surface energy and terrestrial water balances affecting urban ecosystems. In this study, to process the nonlinear changes of climatological variables by leveraging the distinct advantages of Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM), we propose an LSTM-BiLSTM hybrid deep learning model which extracts multi-dimension features of inputs, i.e., backward (future to past) or forward (past to future) to predict ST. This study assessed the climatological variables, i.e., wind speed, wind direction, relative humidity, dew point temperature, and atmospheric pressure impact on ST using five major coastal cities of India: Chennai, Mangalore, Visakhapatnam, Cuddalore, and Cochin. The Recurrent Neural Networks (RNN) and hybrid LSTM-BiLSTM models have effectively predicted ST and outperformed the standalone Artificial Neural Networks (ANN), LSTM, and BiLSTM models. The RNN and LSTM-BiLSTM models have performed better in predicting ST for Mangalore (Nash-Sutcliffe efficiency (NSE)¼0.91), followed by Cochin (NSE¼0.89), Chennai (NSE¼0.88), Cuddalore (NSE¼0.88), and Vishakhapatnam (NSE¼0.81). The hybrid data-driven modeling framework indicated that coupling the LSTM and BiLSTM models were proven effective in predicting the ST of coastal cities

@inproceedings{bib_Rese_2021, AUTHOR = {Maddu, Rajesh and Shaik, Rehana }, TITLE = {Reservoir Inflow Forecasting Based on Gradient Boosting Regressor Model - A Case Study of Bhadra Reservoir, India}, BOOKTITLE = {International Conference, Asia Oceania Geosciences Society}. YEAR = {2021}}

Reservoirs are essential infrastructures in human life. It provides water supply, flood control, hydroelectric power supply, navigations, irrigation, recreation, and other functionalities. To provide these services and resources from the reservoir, it’s necessary to know the reservoir system's inflow. The Machine Learning (ML) techniques are widely acknowledged to forecast the inflow into the reservoir system. In this paper, the popular ML technique, Gradient Boosting Regressor (GBR), is used to predict the reservoir system's inflow. This technique has been applied to the Bhadra reservoir of India at a daily time scale. In this study, the effect and complex relationship of climate phenomenon indices with inflow has been considered. The considered climate phenomenon indices are (1) Arctic Oscillation (AO), (2) East Pacific/North Pacific Oscillation (EPO), (3) North Atlantic Oscillation (NAO), (4) Extreme Eastern Tropical Pacific SST (NINO1+2), (5) Eastern Tropical Pacific SST (NINO3), (6) Central Tropical Pacific SST (NINO4), (7) East Central Tropical pacific SST (NINO34), (8) Pacific North American Index (PNA), (9) Southern Oscillation Index (SOI), (10) Western Pacific Index (WP), (11) Seasonality. In this paper, different parameter settings have been discussed on the models’ performances. The analysis of the GBR method for the Bhadra reservoir includes the number of estimators, maximum depth. The results indicate that the GBR model can capture the inflow's peaks and droughts into the reservoir systems. The study demonstrates how ML methods can be used to generate accurate reservoir inflow predictions.

@inproceedings{bib_Pred_2021, AUTHOR = {Maddu, Rajesh and Shaik, Rehana }, TITLE = {Prediction of river water temperature using machine learning algorithms: a tropical river system of India}, BOOKTITLE = {International Conference, Asia Oceania Geosciences Society}. YEAR = {2021}}

Machine learning (ML) has been increasingly adopted due to its ability to model complex and nonlinearities between river water temperature (RWT) and its predictors (e.g., Air Temperature, AT). Most of these ML approaches have been applied using average AT without any detailed sensitivity analysis of other forms of AT (e.g., maximum and minimum). The present study demonstrates how new ML approaches, such as ridge regression (RR), K-nearest neighbors (KNN) regressor, random forest (RF) regressor, and support vector regression (SVR), can be coupled with Sobol’ global sensitivity analysis (GSA) to predict accurate RWT estimates with the most appropriate form of AT. Furthermore, the proposed ML approaches have been combined with the Ensemble Kalman Filter (EnKF), a data assimilation (DA) technique to improve the predicted values based on the measured data. The proposed modelling framework’s effectiveness is demonstrated with a tropical river system of India, Tunga-Bhadra River, as a case study. The SVR has been noted as the most robust ML model to predict RWT at a monthly time scale compared with daily and seasonal. The study demonstrates how ML methods can be coupled with a global sensitivity algorithm and DA techniques to generate accurate RWT predictions in river water quality modelling.

@inproceedings{bib_Esti_2021, AUTHOR = {Ayaz, Adeeba and Rajesh, Maddu and Singh, Shailesh Kumar and Shaik, Rehana }, TITLE = {Estimation of reference evapotranspiration using machine learningmodels with limited data}, BOOKTITLE = {AIMS Geosciences}. YEAR = {2021}}

: Reference Evapotranspiration (ET0) is a complex hydrological variable defined by various climatic variables affecting water and energy balances and critical factors for crop water requirements and irrigation scheduling. Conventionally ET0 is calculated by various empirical methods based on rigorous climatic data. However, there are many places where various climatic data may not available for ET0 estimation. The objective of this study is to evaluate different machine learning (ML) techniques to estimate ET0 with minimal climatic inputs. In this study, FAO56 Penman-Monteith model was considered as the standard model and different ML models based on A Long short-term memory neural networks (LSTM), Gradient Boosting Regressor (GBR), Random Forest (RF) and Support Vector Regression (SVR) were developed to estimate ET0 with climatic variables as input parameters. These models were evaluated in two different climatic regions, Hyderabad in India and Waipara in New Zealand. The results indicated that 99 % accuracy could be achieved with all climatic input, whereas accuracy drops to 86% with fewer data. LSTM model performed better than other ML models with all input combinations at both the stations, followed by SVR and RF. Both LSTM and SVR models have been noted as the most robust ML models for estimating ET0 with minimal climate data. Even though the excellent performance can be achievable when all input variables are used, the study, however, found that even a three-parameter combination (Temperature, Wind Speed and Relative Humidity values) or two-parameter combination (Temperature and Relative Humidity, Temperature and Wind Speed) can also be promising in ET0 estimation. The presented study will help to estimate ET0 for data scare regions, which is vital for agricultural water management in semi-arid climates.

@inproceedings{bib_Pred_2021, AUTHOR = {M, RAJESH and Shaik, Rehana }, TITLE = {Prediction of river water temperature using machine learning algorithms: a tropical river system of India}, BOOKTITLE = {Journal of Hydroinformatics}. YEAR = {2021}}

Machine learning (ML) has been increasingly adopted due to its ability to model complex and non-linearities between river water temperature (RWT) and its predictors (e.g., Air Temperature, AT). Most of these ML approaches have been applied using average AT without any detailed sensitivity analysis of other forms of AT (e.g., maximum and minimum). The present study demonstrates how new ML approaches, such as ridge regression (RR), K-nearest neighbors (KNN) regressor, random forest (RF) regressor, and support vector regression (SVR), can be coupled with Sobol’ global sensitivity analysis (GSA) to predict accurate RWT estimates with the most appropriate form of AT. Furthermore, the proposed ML approaches have been combined with the Ensemble Kalman Filter (EnKF), a data assimilation (DA) technique to improve the predicted values based on the measured data. The proposed modelling …

@inproceedings{bib_Impa_2021, AUTHOR = {Shaik, Rehana and MONISH, N TINKU }, TITLE = {Impact of potential and actual evapotranspiration on drought phenomena over water and energy-limited regions}, BOOKTITLE = {Theoretical and Applied Climatology volume}. YEAR = {2021}}

Understanding the relevance of potential (PET) and actual evapotranspiration (AET) in the drought characterization over energy and water-limited regions is unexplored. The present study tries to restructure the Standardized Precipitation Evapotranspiration Index (SPEI) with AET to represent the anomalies of actual water availability in addition to precipitation over India. The AET is estimated using the Budyko hypothesis at annual scale which was validated with Global Land Evaporation Amsterdam Model (GLEAM) satellite-based AET data. AET-based drought index was in more agreement with remote sensing-based drought severity index (DSI) for major drought events compared to PET-based drought index for water-limited zone compared to energy-limited. For water-limited zones, the PET-based drought index has overestimated the drought intensities, while for energy-limited zones such effect is not …

@inproceedings{bib_Deve_2021, AUTHOR = {Shaik, Rehana and NAIDU, GALLA SIREESHA }, TITLE = {Development of Hydro-Meteorological Drought Index under Climate Change – Semi-Arid River Basin of Peninsular India}, BOOKTITLE = {Journal of Hydrology}. YEAR = {2021}}

Univariate meteorological drought indices are inadequate to represent the complexity of hydrological conditions under the intensification of hydrological cycle due to climate change at catchment scale. In this study, Stand ardised Precipitation Actual Evapotranspiration Index (SPAEI) was proposed, which can combine both meteo rological and hydrological drought characteristics at catchment scale. The proposed new drought index considers the hydrologically calibrated AET to account for the water use in addition to meteorological effect. The proposed hydrometeorological drought index was potential in identifying meteorological and hydrological drought events accounting for the time-lag effects and comparable with Global Land Evaporation Amsterdam Model (GLEAM) remote sensing AET data-based drought index. The PET based drought index of SPEI, which is based on energy demand, has shown intensified drought characteristics compared to SPAEI, which is based on both energy de mand and available moisture supply and can be a promising variable in the drought estimation. The climate change projections of precipitation and temperatures downscaled using statistical downscaling model based on K-means clustering, Classification and Regression Trees and Support Vector Regression were used using three General Circulation Model outputs. Intensified drought characteristics under climate change has been predicted over Krishna River basin, India, in terms of increase of drought areal extent of about 25%-31%, with increase of drought frequency as 5 years per 20 years and durations as 4–5 months based on the proposed hydrometeoro logical drought index of SPAEI.

@inproceedings{bib_Erra_2020, AUTHOR = {Chaudhary, Shushobhit and Dhanya, C. T. and Kumar, Arun and Shaik, Rehana }, TITLE = {Erratum for “Water Quality–Based Environmental Flow under Plausible Temperature and Pollution Scenarios}, BOOKTITLE = {Journal of Hydrologic Engineering}. YEAR = {2020}}

The following corrections should be made to the original paper: In the “Eflow Estimation Based on Hydrological and Hydraulic Factors” subsection of the “Results and Discussion” section: • In the sentence beginning with “Hydrological Eflow recommendations for maintaining good health over the Yamuna River were estimated to lie between 986.4 and 2,959.3 m3=s,” “986.4 and 2,959.3 m3=s” should be replaced with “27.9 m3=s (986.4 ft3=s) and 83.8 m3=s (2,959.3 ft3=s).” • In the sentence published as, “The minimum Eflow to be maintained in the river during the dry season is 3,945.7 m3=s for the Yamuna and 5.8 m3=s for the Bhadra,” “3,945.7 m3=s” should be replaced with “111.7 m3=s (3,945.7 ft3=s).” In the Supplemental Data: • In Table S3, the values in the columns under “Eflow of Yamuna River (m3=s)” are updated. The published paper contained values in ft3=s, which are retained in brackets. The values of Eflow are converted to m3=s as shown in the revised Table S3. • In Table S5, the “Yamuna” column under “Recommended Eflow (m3=s)” contains values in ft3=s in the published paper. The values of flow are converted in m3=s as shown in the revised Table S5. The values in ft3=s are retained in brackets. • In Fig. S2(a), the y-axis label of “Discharge (cumecs)” should be replaced with “Discharge (cusecs).” The corrected Fig. S2 is provided in the Supplemental Data. In the subsection “Yamuna River (Delhi Segment)” under the section “Description of Study Regions,” “Uttaranchal” should become “Uttarakhand.” In Fig. 2(a), the labels for Drains 15 and 16 should be interchanged. The revised figure is provided herein.

@inproceedings{bib_Seis_2020, AUTHOR = {KISHORE, VASALA RAVI and Mohanty, Supriya and Shaik, Rehana }, TITLE = {Seismic Performance of Soil-Ash and Soil-Ash-Foundation System: A Parametric Study}, BOOKTITLE = {International Journal of Geotechnical Earthquake Engineering}. YEAR = {2020}}

n this study, a 3D seismic response of soil deposit, soil-ash deposit and soil-ash-foundation system was investigated. Homogeneous sand deposit of 80m × 9m × 20m was initially analyzed. A pond ash layer is on top of the sand deposit with varying thicknesses and the efficiency of the pond ash layer on the sand deposit was evaluated for its best suitability. The optimum sand-ash deposit overlain by a shallow foundation has been analysed under the excitation of the Nepal (Mw:7.8) and North East India earthquake (Mw:7.5). A seismic response analysis was performed using finite element software PLAXIS3D. The finite element model adopted for the present study has been validated using 1D nonlinear ground response analysis programs. e.g. DEEPSOIL and Cyclic1D. Results of the response analysis have been determined in terms of acceleration, displacement, excess pore pressure, and excess pore pressure ratio. It was observed that, the sand-pond ash-foundation system experienced liquefaction when excited under the Nepal earthquake motion whereas it is safe against the North East India earthquake.

@inproceedings{bib_Mode_2020, AUTHOR = {NAIDU, GALLA SIREESHA and MURLIDHAR, PORWAL PRATIK and Shaik, Rehana }, TITLE = {Modelling hydrological responses under climate change using machine learning algorithms – semi-arid river basin of peninsular India}, BOOKTITLE = {H2Open Journal}. YEAR = {2020}}

Catchment scale conceptual hydrological models apply calibration parameters entirely based on observed historical data in the climate change impact assessment. The study used the most advanced machine learning algorithms based on Ensemble Regression and Random Forest models to develop dynamically calibrated factors which can form as a basis for the analysis of hydrological responses under climate change. The Random Forest algorithm was identified as a robust method to model the calibration factors with limited data for training and testing with precipitation, evapotranspiration and uncalibrated runoff based on various performance measures. The developed model was further used to study the runoff response under climate change variability of precipitation and temperatures. A statistical downscaling model based on K-means clustering, Classification and Regression Trees and Support Vector …

@inproceedings{bib_Expe_2020, AUTHOR = {REDDY, MEEGADA V RAVI KISHORE and Shaik, Rehana and Mohanty, Supriya }, TITLE = {Experimental Investigation on Dynamic Characterization of Equi-Proportionate Silt–Sand Range Pond Ash at High Strain}, BOOKTITLE = {International Journal of Geosynthetics and Ground Engineering}. YEAR = {2020}}

In the present study, it was attempted to investigate the cyclic resistance of equi-proportionate silt–sand range pond ash (with 50% fines) at relatively high shear strains using the strain-controlled cyclic triaxial test. The cyclic triaxial tests have been performed considering the effect of relative compaction (97–99%), cyclic shear strain (0.6–1.35%), frequency of loading (0.3–1 Hz) and effective confining pressure (50–100 kPa) on cyclic resistance of pond ash. Dynamic characteristics such as dynamic shear modulus and damping ratio of equi-proportionate silt–sand range pond ash was evaluated for all the parameters considered at high shear strain. The maximum value of the dynamic shear modulus and damping ratio of pond ash observed in this study was 6534.8 kPa and 23.64%, respectively. The dynamic shear modulus and damping ratio of pond ash was decreased from 6534.8 to 5023.87 kPa and 23.64 to 14.17 …

@inproceedings{bib_Clos_2020, AUTHOR = {Chaudhary, Shushobhit and Dhanya, C. T. and Shaik, Rehana and Kumar, Arun }, TITLE = {Closure to “Water Quality–Based Environmental Flow under Plausible Temperature and Pollution Scenarios}, BOOKTITLE = {Journal of Hydrologic Engineering}. YEAR = {2020}}

We thank the respected discussers for appreciating the relevance of the present work, critically analyzing it, and bringing forth the possible constraints in its applicability. This discussion provides us an opportunity to elaborate the point related to the scope and applicability of original work, especially with focus on the hydrological community that otherwise would possibly remain unclarified. Because there are two discussions of our paper, we have addressed them in two different sections.

@inproceedings{bib_Wate_2020, AUTHOR = {Chaudhary, Shushobhit and Dhanya, C. T. and Kumar, Arun and Shaik, Rehana }, TITLE = {Water Quality-Based Environmental Flow under Plausible Temperature and Pollution Scenarios (vol 17, pg 216, 2019)}, BOOKTITLE = {Journal of Hydrologic Engineering}. YEAR = {2020}}

This study aimed to estimate water quality–based minimum environmental flow (Eflow) of a river under the impact of different plausible scenarios. The water quality model QUAL2K was deployed to simulate levels of dissolved oxygen (DO) and biochemical oxygen demand (BOD) in a river during the dry season. Hypothetical scenarios of pollution were generated by varying BOD in contributing drains, and climate change scenarios were generated by altering air temperature over the study region. DO and BOD levels were simulated under the impact of various scenarios. Corresponding to each scenario, minimum Eflow throughout the river stretch—that is, the headwaters flow (quantity and quality) meeting desirable river water quality standards (as proposed by the Indian Central Pollution Control Board)— was explored. Water quality charts were developed for direct and user-friendly estimation of Eflows. The proposed approach was applied to the Yamuna and Bhadra rivers in India. The results reveal the inefficiency of existing river flow conditions in maintaining permissible water quality standards. Adverse effects of pollution load, upstream diversions, and climate change are highlighted. DOI: 10.1061/(ASCE) HE.1943-5584.0001780. © 2019 American Society of Civil Engineer

@inproceedings{bib_Mode_2020, AUTHOR = {Shaik, Rehana and NAIDU, GALLA SIREESHA and MONISH, N TINKU and , }, TITLE = {Modeling hydro-climatic changes of evapotranspiration over a semi-arid river basin of India}, BOOKTITLE = {JOURNAL OF WATER AND CLIMATE CHANGE}. YEAR = {2020}}

Parametric models of actual evapotranspiration (AET) based on precipitation (P) and potential evapotranspiration (PET) are region-specific and purely climate-induced and limited to represent the hydrological water balances. Basin-averaged model parameters considering P, AET, and runoff (R) using a machine learning algorithm, ensemble regression model, is proposed. Hydrologically calibrated model parameters allowed the study of AET under alterations of water use for current and for future scenarios under climate change. The effect of climate, water, and land use changes on AET was studied for the post-change period of 2004–2014 compared to pre-change period of 1965–2003 over Krishna river basin (KRB), India. The AET has increased under climate and water use changes while there is both increase and decreases of AET under land use changes for post-change period compared to pre-change period over the basin. Severe water shortages were estimated under pronounced increase of temperature (1.29 C) compared to precipitation increase (2.19%) based on Coordinated Regional Downscaling Experiment (CORDEX) projections for the period 2021–2060. Hydrologically induced AET changes were more pronounced than climate for current climate; whereas climate-induced AET changes were found to be more prominent for projected climate signals over the basin

@inproceedings{bib_Clos_2020, AUTHOR = {Chaudhary, Shushobhit and Dhanya, C. T. and Kumar, Arun and Shaik, Rehana }, TITLE = {Closure to “Water Quality–Based Environmental Flow under Plausible Temperature and Pollution Scenarios”}, BOOKTITLE = {Journal of Hydrologic Engineering}. YEAR = {2020}}

This study aimed to estimate water quality–based minimum environmental flow (Eflow) of a river under the impact of different plausible scenarios. The water quality model QUAL2K was deployed to simulate levels of dissolved oxygen (DO) and biochemical oxygen demand (BOD) in a river during the dry season. Hypothetical scenarios of pollution were generated by varying BOD in contributing drains, and climate change scenarios were generated by altering air temperature over the study region. DO and BOD levels were simulated under the …

@inproceedings{bib_Esti_2020, AUTHOR = {Shaik, Rehana and MONISH, N TINKU and NAIDU, GALLA SIREESHA }, TITLE = {Estimation of annual regional drought index considering the joint effects of climate and water budget for Krishna River basin, India}, BOOKTITLE = {Environmental Monitoring and Assessment}. YEAR = {2020}}

The Standardized Precipitation and Evapotranspiration Index (SPEI) became one of the popular drought indices due to the consideration of difference between precipitation (P) and potential evapotranspiration (PET), which represents the energy-based climatic water balance. Implementation of actual evapotranspiration (AET), which accounts for both water and energy-based climatic evaporative demand in drought characterization studies, is limited. This study proposes a meteorological drought index with the structure of the SPEI and actual evapotranspiration modeled with empirical formulations and remote sensing data integrated with surface energy models at annual scale. The proposed drought index imposes the effect of precipitation, PET, and AET using operational meteorological data sets of precipitation and temperatures. The present study aimed to test how a drought index based on PET and P can outperform with the inclusion of AET at a river basin scale at 12-month scale. The proposed hypothesis was tested considering Krishna River basin, India, as a case study for which most of the basin is in arid climate. The performance of drought indices was compared using historical droughts in terms of severity, areal extent, frequency, and duration based on empirical AET models along with satellite-based land surface ET data-based drought indices. The proposed AET-based drought indices have effectively captured the historical drought years over the Krishna River basin. The empirical AET formulation-based drought index was identified as a more reliable measure in the estimation of drought characteristics by comparing with satellite-based land surface AET-based drought index. The AET-based drought indices were able to drive the areas into moderate, which or otherwise categorized under severe drought regions with PET-based drought indices. Inclusion of AET in the drought characterization along with precipitation and PET can drive the highly intensified drought events determined by SPEI into moderate and less frequent droughts with short durations over a large river basin with arid climate.

@inproceedings{bib_Unce_2020, AUTHOR = {Shaik, Rehana and Rajulapati, Chandra Rupa and Ghosh, Subimal and Karmakar, Subhankar and Mujumdar, Pradeep }, TITLE = {Uncertainty Quantification in Water Resource Systems Modeling: Case Studies from India}, BOOKTITLE = {Water}. YEAR = {2020}}

Regional water resource modelling is important for evaluating system performance by analyzing the reliability, resilience and vulnerability criteria of the system. In water resource systems modelling, several uncertainties abound, including data inadequacy and errors, modeling inaccuracy, lack of knowledge, imprecision, inexactness, randomness of natural phenomena, and operational variability, in addition to challenges such as growing population, increasing water demands, diminishing water sources and climate change. Recent advances in modelling techniques along with high computational capabilities have facilitated rapid progress in this area. In India, several studies have been carried out to understand and quantify uncertainties in various basins, enumerate large temporal and regional mismatches between water availability and demands, and project likely changes due to warming. A comprehensive review of uncertainties in water resource modelling from an Indian perspective is yet to be done. In this work, we aim to appraise the quantification of uncertainties in systems modelling in India and discuss various water resource management and operation models. Basic formulation of models for probabilistic, fuzzy and grey/inexact simulation, optimization, and multi-objective analyses to water resource design, planning and operations are presented. We further discuss challenges in modelling uncertainties, missing links in integrated systems approach, along with directions for future.

@inproceedings{bib_Spat_2019, AUTHOR = {Shaik, Rehana and NAIDU, GALLA SIREESHA and , Nellibilli Tinku Monish }, TITLE = {Spatiotemporal Variations of Precipitation and Temperatures Under CORDEX Climate Change Projections: A Case Study of Krishna River Basin, India}, BOOKTITLE = {Contemporary Environmental Issues and Challenges in Era of Climate Change}. YEAR = {2019}}

The Earth’s climate is not static; it changes according to the natural and anthropogenic climate variability. Anthropogenic forcing due to increase of greenhouse gases in the atmosphere has driven changes in climate variables globally. Changes in climatological variables have severe impact on global hydrological cycle affecting the severity and occurrence of natural hazards such as floods and droughts. Estimation of projections under climate signals with statistical and dynamic downscaling models and integration with water resource management models for the impact assessment have gained much attention. The fine-resolution climate change predictions of dynamic regional climate model (RCM) outputs, which include regional parameterization, have been widely applied in the hydrological impact assessment studies. Advancement of the Coordinated Regional Downscaling Experiment (CORDEX) program has enabled the use of RCMs in regional impact assessment which has progressed in recent years. CORDEX model outputs were considered to be valuable in terms of establishing large ensembles of climate projections based on regional climate downscaling all over the world. However, the simulations of RCM outputs have to be evaluated to check the reliability in reproducing the observed climate variability over a region. The present study demonstrates the use of bias-corrected CORDEX model simulation in analyzing the regional-scale climatology at river basin scale, Krishna river basin (KRB), India. The precipitation and temperature simulations from CORDEX models with RCP 4.5 were evaluated for the historical data for the period of 1965 to 2014 with India Meteorological Department (IMD) gridded rainfall and temperature data sets cropped over the basin. The projected increase

@inproceedings{bib_Mode_2019, AUTHOR = {Shaik, Rehana and Munoz-Arriola, Francisco and Rico, Daniel A. and Bartelt-Hunt, Shannon L. }, TITLE = {Modelling Water Temperature’s Sensitivity to Atmospheric Warming and River Flow}, BOOKTITLE = {Environmental Biotechnology: For Sustainable Future.}. YEAR = {2019}}

River water bodies serve as prominent water sources for various purposes ranging from drinking water, waste load allocation, irrigation, hydropower generation and ecosystem services. Human activities and natural processes require a balanced water supply and demand, while population growth, land use and climate change are the external forces which try to change the stream and river water quan-tity and quality. Water temperature is an inherent property of its quality and a con-trolling factor of health of freshwater environments. It is often considered as a driver of metabolic activity in water bodies, which influence the biological and chemical processes affecting the metabolic responses from organisms to ecosystems. The present work aims to explore sources of predictability of river water temperature (RWT) as a keen driver of hydrological and ecological processes at multiple scales. Increasing RWT in response to climate change and local-to-regional anthropogenic activities result in decreasing dissolved oxygen (DO) levels and anaerobic condi-tions in the aquatic system, thereby affecting marine life and the consequent avail-ability of food, reproduction and migration. An assessment of integrated RWT and streamflow fluctuations is proposed to evidence biological activity, chemical specia-tion, oxygen solubility and self-purification capacity of a river system and fluctua-tions of flows responsive to hydro-climate pulses. The independent and integrated contributions of air temperatures and flow fluctuations to RWT in the Missouri River near Nebraska City, USA, represent the stream responses to global raising temperatures. To quantify the contributions of multiple variations of predictor vari-ables in the air-water interfaces to RWT variability, we use a multiple regression. The performance of the model was tested along Missouri River near Nebraska City, USA, using historical series of daily river water temperature, air temperature and river discharges for the 1947–2014 period. A sensitivity analysis on river water temperature is performed, under air temperature increase of +2 °C, +4 °C and + 6 °C with a decrease of discharge of ±20%. Overall, the increase of RWT for the Missouri River is observed as about 2.76 °C under various air temperature and discharge changes when compared with the observed conditions at mean annual scale. The study results provide a comprehensive analysis of the impacts of river discharge and air temperature changes under climate change over RWT.

@inproceedings{bib_Wate_2019, AUTHOR = {Chaudhary, Shushobhit and Dhanya, C. T. and Kumar, Arun and Shaik, Rehana }, TITLE = {Water quality–based environmental flow under plausible temperature and pollution scenarios}, BOOKTITLE = {Journal of Hydrologic Engineering}. YEAR = {2019}}

This study aimed to estimate water quality–based minimum environmental flow (Eflow) of a river under the impact of different plausible scenarios. The water quality model QUAL2K was deployed to simulate levels of dissolved oxygen (DO) and biochemical oxygendemand (BOD) in a river during the dry season. Hypothetical scenarios of pollution were generated by varying BOD in contributing drains,and climate change scenarios were generated by altering air temperature over the study region. DO and BOD levels were simulated under theimpact of various scenarios. Corresponding to each scenario, minimum Eflow throughout the river stretch—that is, the headwaters flow(quantity and quality) meeting desirable river water quality standards (as proposed by the Indian Central Pollution Control Board)—was explored. Water quality charts were developed for direct and user-friendly estimation of Eflows. The proposed approach was applied to the Yamuna and Bhadra rivers in India. The results reveal the inefficiency of existing river flow conditions in maintaining permissible water quality standards. Adverse effects of pollution load, upstream diversions, and climate change are highlighted.

@inproceedings{bib_Comp_2019, AUTHOR = {Satish, Nagalapalli and Rangarajan, Sathyanathan and Thattai, Deeptha and Shaik, Rehana }, TITLE = {Comparison of Historical Precipitation Data between Cordex Model and Imd Over Malaprabha River Basin, Karnataka State, India}, BOOKTITLE = {International Journal ofInnovative Technology and Exploring Engineering}. YEAR = {2019}}

Validation of precipitation data is important for hydrological modeling. Though there are many models available, rainfall prediction is difficult due to various uncertainties. This study is an attempt to compare and assess the Coordinated Regional Climate Downscaling Experiment (CORDEX) model data and India Meteorological Department (IMD) observed gridded data over Malaprabha river basin, Karnataka state, India. Both gridded data sets were downscaled at 0.25× 0.25 resolution and then processed into a 12× 115 matrix form by using QGIS (2.18. 24) and MATLAB (R2003a). These two products were compared over the period 1961-2015 to evaluate their behavior in terms of fitness by using statistical parameters such as NSE, D, R, MAE, MBE and RMSE values. Results showed that out of 16 grid points, fourteen grid points showed medium correlation ranging between 0.30 and 0.49.

@inproceedings{bib_Rice_2019, AUTHOR = {POOJA, AMBARAKONDA and Mohanty, Supriya and Shaik, Rehana }, TITLE = {Rice Husk Ash and Quarry Waste Mixture as Shell Material in Earth Dam: Experimental and Numerical Investigations}, BOOKTITLE = {International Journal of Geosynthetics and Ground Engineering}. YEAR = {2019}}

In the present study an attempt has been made to propose the application of rice husk ash (RHA), by strengthening it using quarry waste (QW) as additive material in varying percentages of weight. Geotechnical properties of RHA and its combinations with quarry waste (RQ10, RQ20, RQ30, RQ40, RQ50 and RQ60) have been studied by performing grain size analysis, specific gravity, compaction, permeability and triaxial tests. The maximum increase in density, shear strength of RHA upon the addition of quarry waste was observed to be about 45.6% and 39%, respectively. The permeability of RHA, RQ mixes was found to be in fine sand range and hence their application as shell material in earth dam was proposed. The combination RQ50, with equal amounts of RHA and QW was found to exhibit maximum shear strength and was thus considered as the optimum mix. To assess the suitability of RQ50 as shell material in earth dam, numerical analysis of dam system was performed under seismic loading using FEM software Plaxis2D. The seismic behavior of dam system under the application of Nepal input motion was found satisfactory showing high shear resistance and safety against liquefaction. From the outcomes of laboratory and numerical investigations, RHA-QW mixture has been considered as a promising material to be used in earth dam enabling the bulk utilization of both the waste materials.

@inproceedings{bib_Regi_2019, AUTHOR = {Shaik, Rehana and Polepalli, Krishna Reddy and N, Sai Bhaskar Reddy and Daud, Abdul Raheem and Saboory, Shoaib and Khaksari, Shoaib and SK, Tomer }, TITLE = {Regional scale spatiotemporal trends of precipitation and temperatures over Afghanistan}, BOOKTITLE = {Climatic Change}. YEAR = {2019}}

Afghanistan is the most vulnerable to climate extremes related hazards, including droughts and floods that have caused huge impact on the socio-economic development of the country. The present study analysed the observed precipitation and temperature trends for seven agro-climatic zones of Afghanistan over the period 1951 to 2006 with Asian Precipitation-Highly-Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE). The trend analysis was performed on daily data to test the increasing or decreasing rainfall and temperature trends using Mann-Kendall trend test for each agro-climatic zone of Afghanistan. The annual total precipitation has shown an increasing trend for the zones of South, South-West, East and Central, whereas, a decreasing trend has been observed for North, North-East and West zones of Afghanistan. The trend analysis of the precipitation with gridded data sets reveals for most parts of the Afghanistan, the rainfall has been observed to be decreasing. Whereas, an increasing trend of temperatures were observed for all seven agro-climatic zones of Afghanistan.

@inproceedings{bib_Util_2019, AUTHOR = {POOJA, AMBARAKONDA and Mohanty, Supriya and Shaik, Rehana }, TITLE = {Utilization of Quarry Waste and Granulated Rubber Mix as Lightweight Backfill Material}, BOOKTITLE = {Journal of Hazardous, Toxic, and Radioactive Waste}. YEAR = {2019}}

In the present investigation, an attempt has been made to propose a lightweight back fill material using industrial wastes, namely quarry waste and granulated rubber. Granulated rubber was added in varying percentages (5%, 10%, 15%, 20%, 25%, and 30%) to quarry wastefor obtaining lightweight material. Laboratory investigation has been performed in the present study to examine the physical, chemical, andgeotechnical properties of quarry waste and the geotechnical behavior of quarry waste–granulated rubber (QWGR) mixes. Using granulatedrubber as additive, the unit weight of quarry waste material was found to reduce from 21.8 to14.1kN=m3for QWGR30 mix. The friction angleof quarry waste was obtained to be 51.2°, which increased by about 14° with rubber addition for the QWGR30 mix. The permeability of quarrywaste was found to improve from fine sand to coarse sand range with the granulated rubber inclusion, making it an efficient backfill material.QWGR30 mix was found to be an efficient lightweight backfill material with reduced lateral pressure by about 73% and 85% comparedto quarry waste and conventional sand, respectively. The economic savings of the project on backfill material by using QWGR30 over conven-tional sand would be around 8.35%. DOI:10.1061/(ASCE)HZ.2153-5515.0000455.© 2019 American Society of Civil Engineers.

@inproceedings{bib_Suit_2019, AUTHOR = {MONISH, N TINKU and Shaik, Rehana }, TITLE = {Suitability of distributions for standard precipitation and evapotranspiration index over meteorologically homogeneous zones of India}, BOOKTITLE = {Journal of Earth System Science}. YEAR = {2019}}

The Standardised Precipitation and Evapotranspiration Index (SPEI) became one of the popular drought indices in the context of increasing temperatures under global warming in recent periods. The SPEI is estimated by fitting a probability distribution for the difference between precipitation (P) and potential evapotranspiration (PET), which represents the climatic water balance. The choice of an inappropriate probability distribution may lead to bias in the index values leading to distorted drought severity. Till date, none of the studies have focused on the suitability of the probability distribution for SPEI over India. The objective of the present study is to compare and evaluate the performance of a group of candidate probability distributions over seven meteorologically homogeneous zones and all over India using high resolution (0.25°) gridded daily precipitation data from India Meteorological Department (IMD). The Kolmogorov–Smirnov (K–S) test was used to test the goodness-of-fit for (P–PET) and Akaike Information Criterion (AIC) was used to obtain the relative distribution rankings for each grid point. The results of the study suggest that Pearson type III distribution has performed better than other distributions, significantly for shorter time scales and slightly for longer time scales, for each meteorological homogeneous zone based on K–S test. Also, for shorter time scales, Pearson type III distribution has been observed to be significantly better based on AIC with 82.89% and 71.91% grid points for 3 and 6 months, respectively. However, the relative ranking by AIC revealed GEV distribution as the best fit for SPEI values all over India for longer time scales with total grid points as 50.26%, and 58.81% for 12- and 24-month time scales respectively. Pearson type III distribution for shorter time scales (3 and 6 months) and GEV distribution for longer time scales (12 and 24 months) have been identified as the best distributions for fitting SPEI for Indian case study. Comparison of GEV based SPEI with remote sensing-based drought severity index (DSI) for drought events indicated concordance for most of regions in India. Also, SPEI is evaluated to test its capability to represent seasonality and its performance has been compared with Standardised Precipitation Anomaly Index (SPAI) which is known to represent seasonality well.

@inproceedings{bib_Mode_2018, AUTHOR = {Shaik, Rehana and Dhanya, C. T. }, TITLE = {Modeling of extreme risk in river water quality under climate change}, BOOKTITLE = {JOURNAL OF WATER AND CLIMATE CHANGE}. YEAR = {2018}}

A river water quality management model under average climatic conditions may not be able to account for the extreme risk of low water quality which is more prominent under an increase in river water temperature and altered river flows. A modeling framework is developed to assess the risk of river low water quality extremes by integrating a statistical downscaling model based on Canonical Correlation Analysis, risk quantification model based on Frank Archimedean Copula function and multiple logistic regression model integrated with a river water quality simulation model, QUAL2 K. The results reveal that the combination of predicted decrease in low flows of approximately 57% and increase in maximum river water temperatures of approximately 1.2C has shown an increase of about 46% in risk of low water quality conditions for the future scenarios along Tunga-Bhadra River, India. The extreme risk of low water quality is observed to increase by 50.6% for the period 2020– 2040 when compared with the current extreme conditions of 4.5% and average risk conditions of about 3% for the period 1988–2005. The study captured the occurrence of extremes of low water quality with evidence of a strong link between climate and water quality impairment events.

@inproceedings{bib_A_co_2018, AUTHOR = {Shaik, Rehana and MONISH, N TINKU and NAIDU, GALLA SIREESHA }, TITLE = {A comparative analysis of regional drought characterization over Krishna River basin in India using potential and actual evapotranspiration}, BOOKTITLE = {International Conference, Asia Oceania Geosciences Society}. YEAR = {2018}}

Among the extreme events, droughts are the most widespread and slowly developing hydro-meteorological events remain for a long duration affecting natural resources, environment and the people. Few studies included reference evapotranspiration to account for climatic water availability in drought characterization such as Standardized Precipitation–Evapotranspiration Index (SPEI). However, a drought prediction model should consider the actual evapotranspiration to include the physical water availability and land surface changes of a region. The Budyko curve is used to estimate the actual evapotranspiration as a function of the aridity index. The drought index is based on the probability distribution of the difference between precipitation and Actual Evapotranspiration (AET), which represents the measure of the water surplus or deficit for a particular month. The gridded daily precipitation data from India Meteorological Department (IMD) available for the period of 1901 to 2015 at 0.25o X 0.25o resolution and the gridded daily average temperature at a resolution of 1o X 1o resolution was used in the study as temperature observational data sets. The regional drought prediction model developed in the study is applied on Krishna River basin in India. The monthly PET was estimated with the Thornthwaite equation using mean temperatures over the entire Krishna river basin. The AET is estimated at 12 month scale using Budyko equation, which combines the precipitation, AET and PET estimated from Thornthwaite equation. The performance of drought index is evaluated using historical droughts and projected variability under climate change. The results of the study reveal that inclusion of AET in the drought characterization along with PET and precipitation into account can drive the areas into moderate drought that would experience extreme drought under if only PET is considered.

@inproceedings{bib_Comp_2018, AUTHOR = {REDDY, MEEGADA V RAVI KISHORE and Shaik, Rehana and Mohanty, Supriya }, TITLE = {Comparative study of 1d, 2d and 3d ground response analysis of pond ash from odisha under different earthquake motions}, BOOKTITLE = {Indian Geotechnical Conference}. YEAR = {2018}}

In the present study an attempt has been made to study the 1D (One Dimensional), 2D (Two Dimensional) and 3D (Three Dimensional) ground response of pond ash collected from Odisha under different earthquake motions. 1D ground response analysis of pond ash deposit has been carried out by using equivalent linear and nonlinear analysis method using SHAKE2000 and Cyclic1D software respectively. 2D and 3D ground response analysis of pond ash deposit has been carried out by using finite element software Plaxis2D and Plaxis3D respectively. The response analyses of the pond ash deposit have been performed under the excitation of North East India earthquake (Mw-7.5) and Nepal earthquake (Mw-7.8). The results of the response analyses have been presented in terms of acceleration, displacement, excess pore pressure and excess pore pressure ratio. A comparative study between 1D, 2D and 3D ground response analysis has been performed to predict seismic risk of the pond ash site in Odisha.

@inproceedings{bib_Phys_2018, AUTHOR = {Surendrareddy, Chamala and Mohanty, Supriya and Shaik, Rehana }, TITLE = {Physical, chemical and geotechnical characterization of fly ash, bottom ash and municipal solid waste from Telangana State in India}, BOOKTITLE = {International Journal of Geo-Engineering}. YEAR = {2018}}

Utilization of fy ash, bottom ash and municipal solid waste in the feld of geotechnical engineering necessitates determination of its physical, chemical, morphological and engineering properties. This paper presents detailed physical, chemical and geotechnical characterization of fy ash, bottom ash and municipal solid waste from Telangana State in India. Fly ash and bottom ash samples were collected from Kakatiya thermal power station in India. Municipal solid waste (MSW) sample was collected from Borabanda municipal dump site in the city of Hyderabad, India. From the experimental results, it is noticed that specifc gravity of fy ash, bottom ash and municipal solid waste were found to be 1.86, 1.77 and 2.2 respectively. Presence of quartz and mullite; quartz, mullite and calcium carbonate; and quartz and corundum are found to be predominant in fy ash, bottom ash and MSW respectively. The peak friction angle of fy ash, bottom ash and municipal solid waste were found to be 37.02°, 33.77° and 35.23° respectively. Permeability characteristics of fy ash, bottom ash and municipal solid waste were observed to be 1.01E−04 cm/s, 2.01E−04 cm/s and 1.16E−04 cm/s respectively. From the observation of experimental results, it can be concluded that fyash, bottom ash and municipal solid waste from Telangana state are efective material to be used as backfll material, flter material and embankment construction

@inproceedings{bib_Mode_2017, AUTHOR = {A.Naresh, and Shaik, Rehana }, TITLE = {Modeling Stream Water Temperature using Regression Analysis with Air Temperature and Streamflow over Krishna River}, BOOKTITLE = {International Journal of Engineering Technology Science and Research}. YEAR = {2017}}

Predictability of river water temperature is a big argument for many environmental applications, hydrology and ecology research. River water temperature mainly depends on several parameters of water bodies such as streamflow, Dissolved Oxygen (DO), oxidation reduction potential, pollutant temperature, thermal effluent discharges, ground water interactions and its surrounding atmosphere. Commonly, the river water temperature always has been correlated with air temperature as a substitute due to the ease of applicability for rivers with some limitations over detailed meteorological and thermal data. In addition to air temperature the most influencing component of water body is streamflow which is having strong response towards the water temperature. An evaluation of integrated river water temperature and streamflow fluctuations is proposed to evidence biological activity, chemical specimen, oxygen solubility, self-purification capacity of a river system and variation of flows due to hydro-climatic changes. Prediction of river water temperature at various locations over a river basin is vital for the water quality management. The present study aims to estimate the river water temperature (RWT) under air temperature changes and local to regional anthropogenic activities of streamflow over Krishna river basin, India. A standardized multi-linear regression model is developed for predicting river water temperature over various discharge locations of the Krishna river basin. The performance of the model was tested mainly for 8 stations using historical daily data of river temperature, air temperature and river discharges from 1991 to 2005. The attainment of a model is carried out for these stations with air temperature, stream flows are predictors whereas water temperature as predictability component through training, testing. In this study the correlation coefficients between air minimum, maximum and average temperature with river water temperature are determined for selecting the possible air temperature variable to be considered in the analysis. The correlation coefficients are obtained with air minimum temperature (r = 0.541-0.772), air maximum temperature (r= 0.267-0.613) and air average temperature (r = 0.463-0.715) for all stations and it is observed that air minimum has the highest correlation coefficient value and it could be suitable for modeling. The performance of the multi-linear regression model for training and testing was obtained in terms of Root Mean Square Error (RMSE = 0.934 to 4.556), Correlation of determination (R = 0.984 and 0.986).

@inproceedings{bib_Regi_2016, AUTHOR = {Shaik, Rehana }, TITLE = {Regional Hydrologic Impacts Of Climate Change}, BOOKTITLE = {Journal of Hydrology}. YEAR = {2016}}

Climate change could aggravate periodic and chronic shortfalls of water, particularly in arid and semi-arid areas of the world (IPCC, 2001). Climate change is likely to accelerate the global hydrological cycle, with increase in temperature, changes in precipitation patterns, and evapotranspiration affecting the water quantity and quality, water availability and demands. The various components of a surface water resources system affected by climate change may include the water availability, irrigation demands, water quality, hydropower generation, ground water recharge, soil moisture etc. It is prudent to examine the anticipated impacts of climate change on these different components individually or combinedly with a view to developing responses to minimize the climate change induced risk in water resources systems. Assessment of climate change impacts on water resources essentially involves downscaling the projections of climatic variable

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